# Fair Abstractive Summarization of Diverse Perspectives

Yusen Zhang♣ Nan Zhang♣ Yixin Liu†
Alexander Fabbri♢ Junru Liu♠ Ryo Kamoi♣ Xiaoxin Lu♣
Caiming Xiong♢ Jieyu Zhao‡ Dragomir Radev† Kathleen McKeown♡ Rui Zhang♣
♣Penn State University †Yale University ♢Salesforce Research
♠Texas A&M University ‡University of Southern California ♡Columbia University
{yfz5488<EMAIL_ADDRESS>
###### Abstract

People from different social and demographic groups express diverse
perspectives and conflicting opinions on a broad set of topics such as product
reviews, healthcare, law, and politics. A fair summary should provide a
comprehensive coverage of diverse perspectives without underrepresenting
certain groups. However, current work in summarization metrics and Large
Language Models (LLMs) evaluation has not explored fair abstractive
summarization. In this paper, we systematically investigate fair abstractive
summarization for user-generated data. We first formally define fairness in
abstractive summarization as not underrepresenting perspectives of any groups
of people and propose four reference-free automatic metrics measuring the
differences between target and source perspectives. We evaluate five LLMs,
including three GPT models, Alpaca, and Claude, on six datasets collected from
social media, online reviews, and recorded transcripts. Experiments show that
both the model-generated and the human-written reference summaries suffer from
low fairness. We conduct a comprehensive analysis of the common factors
influencing fairness and propose three simple but effective methods to
alleviate unfair summarization. Our dataset and code are available at
https://github.com/psunlpgroup/FairSumm.

Figure 1: An example from the Amazon dataset and a model-generated summary.
The blue/red box displays the positive/negative reviews. The grey box contains
the summary generated by GPT-3.5 (text-davinci-003). The generated summary is
unfair as negative reviews are underrepresented compared with positive
reviews.

##  1 Introduction

Different social and demographic groups of people hold diverse and potentially
even conflicting perspectives and opinions, which can be expressed in user-
generated text data in domains and topics such as product reviews, social
debates, healthcare, law, and politics Shandilya et al. (2018); Bražinskas et
al. (2020a); Zhang et al. (2016); Huang et al. (2023). We formalize fair
summarization of diverse perspectives as generating a fair summary for user-
generated data by providing an accurate and comprehensive view of various
perspectives from these groups. For example, Figure 1 shows the reviews about
an Amazon product. The summary generated by GPT-3.5 (Ouyang et al., 2022)
unfairly represents sentiments as it summarizes more content from positive
reviews than negative, ignoring negative groups and thus misleading the
reader.

However, existing summarization metrics cannot adequately measure fairness. On
the one hand, the summarization metrics such as ROUGE (Lin, 2004) and
BERTScore (Zhang et al., 2020) fail to measure fairness for two reasons.
First, they are not inherently designed for quantifying and measuring fairness
directly, but rather measuring similarity between system outputs and
references based on n-gram overlapping or embeddings. Second, the reference
summaries can also be biased. On the other hand, current work on summarization
(Shandilya et al., 2018) and LLM fairness evaluation (Chang et al., 2023) has
not explored fair abstractive summarization which is more dominating than
extractive summarization with the development of LLMs OpenAI (2023).

In this work, we present the first study that systematically defines,
quantifies, and benchmarks fairness in abstractive summarization using large
language models in a wide range of domains and topics. First, we define fair
abstractive summarization that takes into consideration a social attribute,
such as gender, and aim to generate summaries that are “fair” with respect to
this attribute. We curate a benchmark PerspectiveSumm (Table 1) by unifying
and cleaning six existing datasets in domains where unfair summarization is a
significant issue including healthcare, politics, restaurant, product, law,
and policy, which covers social attributes of gender, party, sentiment,
rating, and speaker. The fairness of the summary on these attributes greatly
influences the information and even the opinions of the readers. Next, we
quantify fairness in abstractive summarization as a distribution alignment of
the generated summary and the source text. The distribution is calculated by
ratios of tokens written by different groups of people (e.g. tweets written by
male/female). According to the definition, we propose several metrics to
evaluate fairness, including Binary Unfair Rate (BUR) which measures the
overall fairness in abstractive summarization by counting the number of fair
summaries in the whole dataset, Unfair Error Rate (UER) which measures the
distance between generated summaries and source text by computing how target
distribution differs from source’s. Third, we benchmark fairness in
abstractive summarization. We conduct a comprehensive evaluation of five main-
stream LLMs, including GPTs (text-davinci-003, gpt-turbo-3.5, gpt-4), Alpaca,
and Claude on PerspectiveSumm.

Our results show that GPT-4 shows the least number of unfair summary based on
our definitions. However, even GPT-4 OpenAI (2023) suffers from fairness
problems as the BUR score for GPT-4 on Amazon dataset reaches 74.27%. We also
analyze the common factors that can influence fairness, including summary
length, perspective ratio Shandilya et al. (2018, 2020), and temperature Guo
et al. (2017). Then we discovered three simple but effective ways to increase
the fairness of the summaries including changing temperature, changing
generated length, and appending the definition of fairness to the prompt. The
result shows the BUR decreases from 51.11% to 47.48% on GPT-turbo-3.5 Ouyang
et al. (2022) by providing instructions of fairness definition.

Our contributions are as follows: (1) We propose new metrics for quantifying
and measuring the fairness of abstractive summarization over diverse
perspectives. (2) We collect and unify a comprehensive benchmark for analyzing
fairness in abstractive summarization in various domains and topics. (3) We
conduct comprehensive experiments to investigate the fairness of reference
summaries and summaries generated by popular LLMs finding that both
reference/generated summaries fail to achieve the fairness. (4) We analyze the
factors that influence fairness. We also propose three simple but effective
methods to improve fairness.

Dataset | Domain | Source Form | Collect From | Attributes | Values | # Samples | Max and Avg Length
---|---|---|---|---|---|---|---
Claritin | Healthcare | Social media | twitter.com | Gender | 2 | 1350 | 1087/572.2
US Election | Politics | Social media | twitter.com | Party | 3 | 1350 | 1247/677.7
Yelp | Restaurant | Review | yelp.com | Sentiment | 3 | 1500 | 576/402.1
Amazon | Product | Review | amazon.com | Rating | 5 | 1500 | 531/346.5
Supreme Court | Law | Dialogue | supremecourt.gov | Speakers, Gender, Power | 3-10 | 665 | 2721/1910.9
Intelligence Squared | Public Policy | Debate | opendebate.org | Speakers, Power | 2-10 | 1421 | 3275/1650.7

Table 1: Dataset characteristics in PerspectiveSumm for fair abstractive
summarization. See Appendix B for examples.

##  2 Summarization of Diverse Perspectives with Social Attributes

In this section, we first formulate our task of summarization from diverse
perspectives with social attributes (Section 2.1), then present data
collection of PerspectiveSumm for this task (Section 2.2).

###  2.1 Task Formulation

#### Social Attributes.

A fair abstractive summary should include comprehensive perspectives without
underrepresenting certain groups of people. We use social attributes to
indicate the properties that form groups of people. As shown in Table 1,
social attributes can be gender, party, and sentiment.

#### Summarization with Social Attributes.

After defining social attributes, we give the formal definition of
summarization with social attributes.

#### Definition 2.1 Summarization with Social Attribute.

A summarization dataset $\mathcal{D}$ consists of pairs of source text and
target summary $(\bm{x},\bm{y})$. Here,
$\bm{x}=\\{x_{1},x_{2},\cdots,x_{n}\\}$ is the source text consisting of $n$
token $x_{i}$ and $\bm{y}=\\{y_{1},y_{2},\cdots,y_{m}\\}$ is the target
summary consisting of $m$ tokens $y_{i}$. We consider a social attribute $a$
having $r$ different values $\mathcal{V}=\\{v_{1},v_{2},\cdots,v_{r}\\}$,
$r>1$. Furthermore, we use $v(\cdot)$ to denote the value of a token. We
assume each source unit $x_{i}$ belongs to one value
$v(x_{i})\subset\mathcal{V}$, $|v(x_{i})|=1$, while each token in target is
associated with one or multiple values $v(y_{i})\subseteq\mathcal{V}$,
$|v(y_{i})|\geq 1$. For instance, when considering gender as an attribute $a$
in Twitter review summarization, the $r=2$ possible values are
$\mathcal{V}=\\{\texttt{male},\texttt{female}\\}$. For each tweet token
$x_{i}$ in the source, if it is written by a male, $v(x_{i})=\texttt{male}$,
otherwise $v(x_{i})=\texttt{female}$111To make the question simple, we
consider two genders in our experiments.. For each token $y_{i}$ in the
summary of tweets, it could come from male, female, or both. For instance, if
both party says a product is “good”. Then “good” can be both from male, and
female.

###  2.2 Data Collection

To conduct a comprehensive analysis of fairness, we select and then process
existing datasets to form our fair summarization benchmark PerspectiveSumm. We
follow two main principles to choose six datasets from a large variety of
existing datasets. First is the quality, the source texts need to be human-
written and marked with clear, and precise social attributes. These attributes
are existing meta-data in the dataset except that we use a classifier for
sentiment classification. Second is diversity, the collected datasets need to
cover various domains and perspectives.

Table 1 shows the statistics of PerspectiveSumm. It covers six different
domains with six attributes. It is worth noting that, two dialogue datasets
contain more than one attribute, which emphasizes that people can have diverse
perspectives of fairness on the same input source text. Details of these six
datasets are in Appendix A.

Figure 2: Overview of our proposed metrics. Source/Target Dist. indicates
source/target value distribution.

##  3 Defining Fairness in Summarization

In this section, we provide definitions for fairness and propose metrics to
evaluate it. Figure 2 shows the logic of computation of fairness in
abstractive summarization and corresponding evaluation metrics. The first
stage is to produce the value distribution. Given a sample pair of source and
target text, we first split the source according to the annotated values of
social attributes. Then, we apply Rule/BERT/BART scores to compare target
tokens with source tokens to obtain the value of each token in the summary.
Next, we count the number of tokens for each value in the source and target
and obtain the value distribution of them.

###  3.1 Defining Value Distribution

Computation of source and target value distribution (source/target
distribution for short) is the first step of fairness computation. The formal
definition is listed as follows.

#### Definition 3.1 Value Distribution.

We define the value distribution $\bm{p}$ in three scenarios: (1) the value
distribution in source:
$\bm{p}_{\bm{x}}=[\bm{p}_{\bm{x}}(v_{1}),\bm{p}_{\bm{x}}(v_{2}),\cdots,\bm{p}_{\bm{x}}(v_{r})]$,
where $\bm{p}_{\bm{x}}(v_{k})$ is the proportion of source tokens associated
with value $v_{k}$, $0\leq\bm{p}_{\bm{x}}(v_{k})\leq 1$,
$\sum_{k=0}^{r}\bm{p}_{\bm{x}}(v_{k})=1$. (2) Similarly, the value
distribution in target:
$\bm{p}_{\bm{y}}=[\bm{p}_{\bm{y}}(v_{1}),\bm{p}_{\bm{y}}(v_{2}),\cdots,\bm{p}_{\bm{y}}(v_{r})]$.
(3) a gold value distribution
$\bm{p}_{\bm{g}}=[\bm{p}_{\bm{g}}(v_{1}),\bm{p}_{\bm{g}}(v_{2}),\cdots,\bm{p}_{\bm{g}}(v_{r})]$
which means the distribution that aligned with user’s requirement (Section
3.3).

###  3.2 Calculating Value Distribution

To compute $\bm{p}_{\bm{x}}$, since the meta-data of the dataset shows the
values of each token in the source, it can be acquired by counting the number
of tokens of each value. However, the calculation of $\bm{p}_{\bm{y}}$ cannot
be obtained easily due to the abstract nature of summaries. As abstractive
summarization does not directly copy from the source text, it contains words
that are not in the source but still semantically come from the source. This
makes it difficult to attribute a sentence in the summary to a certain part of
the source. To tackle this, we propose two algorithms for calculating the
target distribution $\bm{p}_{\bm{y}}$:

#### N-gram Matching

For a given k, we scan each k-gram in target text, if this k-gram is also
shown in the source text, we assign this k-gram the value of the source text
it appears. It is worth noting that the k-gram in the target can be assigned
multiple values according to the definition. These metrics can automatically
compute $\bm{p}_{\bm{y}}$ given $\bm{p}_{\bm{x}}$, with no additional
annotation on $\bm{p}_{\bm{y}}$. We use $k=1$ for all experiments. In this
paper, we use RuleScore to represent this matching approach.

#### Neural Matching

We use BARTScore (Yuan et al., 2021), and BERTScore Zhang et al. (2020) to
measure the distance between the target and source. These metrics capture the
similarity in semantics, overcoming the new word matching issues in the n-gram
algorithm Banerjee and Lavie (2005). Specifically, for a given source $\bm{x}$
and target $\bm{y}$, we first split $\bm{x}$ by value. For each
$v_{i}\in\mathcal{V}$, $\bm{x_{i}}=\\{x\in\bm{x}|v(x)=v_{i}\\}$. Let
$r=|\mathcal{V}|$. Then, we compute the correlation between $\bm{x_{i}}$ and
$\bm{y}$ to obtain $\bm{p}_{\bm{y}}$:

$\bm{p}_{\bm{y}}=\text{Softmax}(\text{Score}(\bm{x_{1}},\bm{y}),\cdots,\text{Score}(\bm{x_{r}},\bm{y}))$
(1)

Where Score is BARTScore and BERTScore, and the temperature of Softmax is a
hyperparameter. We pick 0.1 for experiments because it better aligns with
human evaluation (Appendix C.2).

The design of the metrics is different in terms of the following two aspects.
First, they have different granularity. The N-gram score is based on the token
which controls the fairness of the token level. BERT score computes the
similarity of the sentences which is in sentence level. BART score computes
via the entailment of the entire summary. This is on the summary level.
Second, these scores have different advantages. Rule score can be applied to
diverse length source text while the other two scores have length limitations.
BERT score can capture semantic similarity while BART score captures
entailment. These two models rely on the accuracy of the backbone models as
well.

###  3.3 Defining Summarization Fairness

After defining the source/target distribution, the goal of fair summarization
is equivalent to producing a $\bm{p}_{\bm{y}}$ not underrepresenting any value
in $\bm{p}_{\bm{x}}$.

#### Definition 3.3 Summarization Fairness.

We define unfair summary as exhibiting underrepresentation of user groups in
summaries. Given value distributions, we can define two types of fairness: (1)
Ratio Fairness: the target value distribution $\bm{p}_{\bm{y}}$ shall follow
the same value distribution as source $\bm{p}_{\bm{x}}$. In this case,
$\bm{p}_{\bm{g}}=\bm{p}_{\bm{x}}$. (2) Equal Fairness: the target value
distribution $\bm{p}_{\bm{y}}$ shall follow the uniform value distribution
$\bm{p}_{\bm{g}}=[1/r,1/r,\cdots,1/r]$, regardless of source. This
distribution keeps the balance of each value. However, users can define any
$\bm{p}_{\bm{g}}$ to indicate their own idea distribution, not restricted to
Ratio Fairness or Equal Fairness. We mainly discuss ratio fairness in the
experiment section because summarization aims to compress but still follow the
value distribution in the source text Shandilya et al. (2018).

##  4 Evaluating Fairness in Summarization

As shown in Figure 2, we define four types of metrics to evaluate whether the
generated summaries are fair. First, given the source and target distribution
and definition of fairness summary. Binary Unfair Rate gives direct fair-or-
not binary unfair classification results.

#### Definition 3.3 Binary Unfair Rate (BUR).

We define Binary Unfair Rate (BUR), a binary function that outputs 1 if the
sample is unfair; and 0 otherwise. To be specific, we define the summary as
fair if and only if
$\bm{p}_{\bm{y}}(v_{k})\geq\tau\cdot\bm{p}_{\bm{x}}(v_{k})$ holds for each
value $k=1,2,\cdots,r$, where $0\leq\tau\leq 1$ is a tolerance hyperparameter
that is chosen based on application scenarios (Shandilya et al., 2018).
Otherwise, if $\bm{p}_{\bm{y}}(v_{k})<\tau\cdot\bm{p}_{\bm{x}}(v_{k})$, we say
the value $v_{k}$ is underrepresented. Thus

$f_{\text{BUR}}(\bm{x},\bm{y};\tau)=\mathbbm{1}\left(\bigvee_{i=1}^{r}\bm{p}_{\bm{y}}(v_{k})<\tau\cdot\bm{p}_{\bm{\bm{x}}}(v_{k})\right)$
(2)

This metric is either zero to one and it can be averaged over the dataset,
demonstrating the proportion of summaries that are unfair in the dataset.
However, this hard metric may not be able to describe which one is more
unfair/fair if BUR of two samples is the same. Thus, we propose Unfair Error
Rate as a soft metric to compute the sum of the distance to source
distribution of each value.

#### Definition 3.4 Unfair Error Rate (UER).

This metric defines a function $f_{\text{UER}}(\bm{x},\bm{y})$ measuring the
distance between $\bm{p}_{\bm{x}}$ and $\bm{p}_{\bm{y}}$. UER serves as the
supplementary metric for BUR as it shows more fined-grained details compared
with BUR.

$f_{\text{UER}}(\bm{x},\bm{y})=\frac{1}{r}\sum_{k=1}^{r}\max(0,\bm{p}_{\bm{y}}(v_{k})-\bm{p}_{\bm{x}}(v_{k}))$
(3)

UER shows the average percentage of values that are underrepresented.
Furthermore, since people have different tolerance and requirements for
fairness, we propose Area Under the Curve to compute the expected result of
BUR when the tolerance is randomly picked from zero to one.

#### Definition 3.5 Area Under Curve (AUC).

As mentioned, $\tau$ is a hyperparameter chosen according to the scenarios.
However, people may have different assignments of $\tau$ according to their
own experiences. To incorporate different tolerance hyper-parameters, we
propose AUC of fairness threshold (AUC for short) for BUR metric
$f_{\text{AUC}}(\bm{x},\bm{y})$.

$f_{\text{AUC}}(\bm{x},\bm{y})=\int_{0}^{1}f_{\text{BUR}}(\bm{x},\bm{y};\tau)\;d\tau$
(4)

It is a number between zero and one showing the expected BUR before assigning
a fairness threshold $\tau$ to it because we assume that the users evenly pick
the threshold between zero and one. It is worth noting that, practically, we
sample ten points evenly to approximate the integration.

These three metrics cannot show which value is more unfair in one sample.
Thus, we further propose Second-Order Fairness to compute the variance of UER
among all values.

#### Definition 3.6 Second-Order Fairness (SOF).

First, we define UER set $S_{\text{UER}}$ as

$S_{\text{UER}}=\\{\max(0,\bm{p}_{\bm{g}}(v_{k})-\bm{p}_{\bm{x}}(v_{k}))\
|k\in[r]\\}$ (5)

Second-Order Fairness (SOF) measures the unfairness difference among all
values, defined as:

$f_{\text{SOF}}(\bm{x},\bm{y})=\frac{1}{r}\sum_{s\in
S_{\text{UER}}}\left|s-\frac{\sum_{s\in S_{\text{UER}}}s}{r}\right|$ (6)

Inspired by MacQueen et al. (1967), SOF measures the coherence of set
$S_{\text{UER}}$ via computing the average distance to the center $\sum_{s\in
S_{\text{UER}}}s/r$.

If all values’ UER are close to their averaged center, it indicates the
generated summary leans towards certain values. In this case, we say that each
value has a similar possibility to be unfair, so-called second-order fairness.

Tolerance Hyperparameter. Regarding $\tau$, the threshold that decides whether
a value is under-represented is based on the application scenarios (Celis et
al., 2018). When strict fairness is required, such as the political opinions
of two parties, this threshold should be set to a high value, for instance,
$\tau=0.8$ or higher (Shandilya et al., 2018). However, in some other text
summarization tasks in our daily lives, we do not require each value to have
strict constraints, such as product review summarization. In the extreme case,
we only want each value to appear in the summary rather than constraining the
time of appearance, so we can set $\tau=0$. In this paper, we set $\tau=0.8$
for all experiments (Biddle, 2017), if not specified.

Overall Score. The score of a metric
$\text{M}\in\\{\text{BUR},\text{UER},\text{AUC},\text{SOF}\\}$222SOF computes
$S_{\text{UER}}$ on $\mathcal{D}$ first. Then apply Equation 6.  on a dataset
$\mathcal{D}$ is the average corresponding metrics $f_{\text{M}}$ on each
sample:

$\text{M}=\frac{1}{|\mathcal{D}|}\sum_{(\bm{x},\bm{y})\in\mathcal{D}}f_{\text{M}}(\bm{x},\bm{y})$
(7)

| Claritin | US Election | Yelp | Amazon | SupremeCourt | IQ2
---|---|---|---|---|---|---
| BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$
Alpaca★ (7B) |  |  |  |  |  |  |  |  |  |  |  |
N-gram | 51.12 | 7.63 | 82.56 | 7.01 | 34.47 | 3.71 | 74.27 | 5.11 | 70.77 | 5.41 | 82.14 | 4.37
BERTScore | 70.85 | 12.17 | 83.27 | 6.83 | 38.73 | 5.52 | 76.20 | 6.89 | 78.46 | 6.07 | 85.94 | 6.94
BARTScore | 73.84 | 11.13 | 94.31 | 9.72 | 58.93 | 6.11 | 91.80 | 9.48 | 89.23 | 7.09 | 90.78 | 10.32
Average | 65.27 | 10.31 | 86.71 | 7.85 | 44.04 | 5.11 | 80.76 | 7.16 | 79.49 | 6.19 | 86.29 | 7.21
text-davinci-003 (175B) |  |  |  |  |  |  |  |  |  |  |  |
N-gram | 45.41 | 6.88 | 73.04 | 5.22 | 26.87 | 2.93 | 67.80 | 4.14 | 96.09 | 4.28 | 78.11 | 3.89
BERTScore | 69.48 | 11.18 | 82.30 | 6.64 | 40.27 | 4.03 | 78.27 | 5.79 | 96.09 | 4.63 | 88.67 | 6.92
BARTScore | 73.93 | 9.17 | 92.89 | 9.41 | 62.13 | 6.09 | 93.60 | 9.86 | 100.00 | 6.84 | 94.72 | 10.78
Average | 62.94 | 9.08 | 82.74 | 7.09 | 43.09 | 4.35 | 79.89 | 6.60 | 97.39 | 5.25 | 87.17 | 7.20
gpt-turbo-3.5 (175B) |  |  |  |  |  |  |  |  |  |  |  |
N-gram | 51.11 | 7.53 | 72.67 | 5.00 | 21.80 | 3.01 | 63.13 | 4.12 | 96.84 | 4.16 | 72.48 | 3.37
BERTScore | 68.22 | 11.04 | 80.37 | 6.10 | 33.80 | 3.70 | 71.20 | 4.93 | 92.93 | 3.76 | 86.77 | 5.68
BARTScore | 73.56 | 8.98 | 91.11 | 8.35 | 60.33 | 5.30 | 93.33 | 8.40 | 100.00 | 6.00 | 94.30 | 9.15
Average | 64.30 | 9.18 | 81.38 | 6.48 | 38.64 | 4.00 | 75.89 | 5.82 | 96.59 | 4.64 | 84.52 | 6.07
gpt-4 (N/A) |  |  |  |  |  |  |  |  |  |  |  |
N-gram | 50.81 | 7.51 | 65.78 | 4.38 | 20.27 | 2.50 | 59.87 | 3.88 | 95.34 | 4.12 | 79.45 | 3.87
BERTScore | 70.44 | 11.96 | 78.81 | 6.22 | 36.60 | 3.21 | 72.00 | 4.39 | 95.04 | 3.71 | 88.88 | 6.96
BARTScore | 77.85 | 10.34 | 95.19 | 10.36 | 61.40 | 5.46 | 92.47 | 8.20 | 100.00 | 5.87 | 94.79 | 10.00
Average | 66.37 | 9.94 | 79.93 | 6.99 | 39.42 | 3.72 | 74.78 | 5.49 | 96.79 | 4.57 | 87.71 | 6.94

Table 2: Overall results of all datasets and models. We consider 6 datasets, 4
models, and 2 metrics. BUR and UER are better with a lower score $\downarrow$.
Bold indicates the best performance, and underline indicates the second best.
Results show that gpt-4, gpt-turbo-3.5, and text-davinci-003 obtain the best
performance on 6, 4, and 2 metrics, respectively. ★ Alpaca produces a number
of empty strings which we ignore. Thus, the results of Alpaca are not
compatible with other models. Alpaca is open-sourced, while the other three
models are not open-sourced.

##  5 Experiment Results and Analysis

Our results and analysis aim to answer the following research questions:

  * •

RQ 1: How fair are the abstractive summaries generated by LLMs according to
our automatic metrics (Section 5.1)?

  * •

RQ 2: How fair are the existing human-written reference summaries according to
our automatic metrics (Section 5.2)?

  * •

RQ 3: How do humans perceive the fairness of abstractive summaries generated
by LLMs, and how does human evaluation correlate with our automatic metrics
(Section 5.3)?

  * •

RQ 4: How can we dissect the fairness of abstractive summaries generated by
LLMs: using the distribution across values and second order fairness (Section
5.4), analysis on AUC (Section 5.5)?

We use gpt-turbo-3.5 on the Claritin dataset for analysis experiments with
RuleScore as the target matching method, if not specified.

###  5.1 Overall Results

Table 2 shows the results of all models using our metrics. Model details are
listed in Appendix D. We also evaluate metric quality in Appendix G. As shown
in the table, text-davinci-003 achieves the best performance on Claritin, gpt-
turbo-3.5 outperforms other models on US Election, Yelp, SupremeCourt, and
IQ2. gpt-4 obtains the best performance on Amazon dataset. This result shows
the fairness capability of different models varies in different domains and
tasks, while gpt-4 masters more domains than the other two models because it
achieves better performance on three models covering three domains. We observe
that the different models display similar scores on the same dataset although
their unfair summaries vary. (Appendix H).

#### Long Context Results

Different from other models, claude-instant-1 can consume 100k tokens in the
source input. Thus, we prepare a full dataset on IQ2 and SupremeCourt without
segmentation. Table 4 shows the results of Claude model. Compared with short
input, it is more difficult to maintain a fair summary. Thus, the Claude model
also suffers from fairness issues on long input datasets.

| Rule |
---|---|---
| BUR$\downarrow$ | UER$\downarrow$ | SOF$\downarrow$ |
SupremeCourt | 99.07 | 1.77 | 1.23 |
IQ2 | 100.00 | 2.37 | 2.23 |

Table 3: Results of claude-instant-1. Only Rule-based score is leveraged
because the input length exceeds the limit of BERT and BART scores.

| Rule | BERT | BART
---|---|---|---
| BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$
reference summary |  |  |  |  |  |
Amazon | 95.00 | 18.50 | 95.00 | 18.50 | 91.67 | 9.44
Yelp | 68.00 | 26.43 | 53.04 | 7.90 | 56.00 | 10.11
gpt-turbo-3.5 |  |  |  |  |  |
Amazon | 68.33 | 3.97 | 76.67 | 5.99 | 93.33 | 9.51
Yelp | 21.00 | 3.12 | 36.00 | 4.76 | 64.00 | 6.27

Table 4: Comparison between reference summary and summary generated by gpt-
turbo-3.5.

###  5.2 Fairness of Reference Summary

In addition to the model-generated summaries, we also explore the fairness of
the reference summaries which are totally written by humans. We analyze the
fairness of 100 reference summaries and summaries generated by gpt-turbo-3.5
on Amazon and Yelp datasets, respectively. Table 4 shows the comparison
between them. As can be seen, the reference summary also surfers from high BUR
and UER scores, meaning that the human writer cannot maintain the fairness of
the summary as well. Comparing reference summary and GPT-predicted summary,
gpt-turbo-3.5 enjoys better fairness on average. This demonstrates that GPT
models generate summaries with higher quality than human-written ones Tanya
Goyal (2022).

###  5.3 Human Evaluation

We present two sets of human evaluation scores, corresponding to the two
stages of the annotation process. Design details are in Appendix C. In the
first stage, we decompose each sentence into Atomic Content Units Liu et al.
(2022) (ACUs), and then count the proportions of each value in the summary.
After that, similar to all the other methods, BUR and UER scores are leveraged
to compute fairness. We conduct a pilot annotation by asking the annotators to
annotate a small proportion of the dataset to compute the inter-annotator
agreement. The agreement of this stage is 68.97% Krippendroff score showing
the high inter-annotator agreement. Then, we ask the annotator to start
annotation. As shown in Table 5, text-davinci-003 achieves the best
performance on all the metrics in acu-based scores, showing that human favors
text-davinci-003 the most. It is worth noting that, Alpaca achieves fairness
that is similar to that of gpt-turbo-3.5, demonstrating that small models and
large language models can achieve similar fairness.

In the second stage, the annotators are asked to give a rating on the fairness
of the summaries. This is more subjective as annotators can pay different
attention to different ACUs. This serves as another angle of fairness which
gives more space to human’s personal feelings on fairness. The Randolph’s
Kappa score is 0.41, demonstrating the high agreement across 4 annotators. As
shown in Table 5, different from the ACU-based scores, the overall ratings
lean toward turbo models which align with the auto-evaluation metrics.

| Claritin | Yelp | IQ2
---|---|---|---
| BUR$\downarrow$ | UER$\downarrow$ | Rating$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | Rating$\downarrow$ | BUR$\downarrow$ | UER$\downarrow$ | Rating$\downarrow$
Alpaca | 76.47 | 13.10 | 85.00 | 45.00 | 4.67 | 88.24 | 88.88 | 11.83 | 100.00
text-davinci-003 | 58.75 | 8.85 | 51.76 | 15.00 | 2.58 | 64.71 | 60.00 | 10.90 | 100.00
gpt-turbo-3.5 | 82.35 | 10.72 | 45.00 | 42.11 | 3.33 | 62.50 | 80.00 | 8.39 | 100.00
gpt-4 | 76.17 | 13.07 | 80.00 | 40.00 | 2.76 | 88.24 | 100.00 | 15.74 | 100.00

Table 5: Human evaluation results. BUR and UER scores are computed using acu-
based scores. Rating scores are computed using rating-based scores.

###  5.4 Distribution across Values

Figure 3: Detailed distribution of Male and Female.

We dissect the fairness of abstractive summaries generated by LLMs using the
distribution across values. Figure 3 shows the distribution of males and
females on Claritin dataset. X-axis is the target distribution divided by
source distribution $\bm{p}_{\bm{y}}(v_{k})/\bm{p}_{\bm{x}}(v_{k})$. As can be
seen, about 20% of gpt-turbo-3.5 outputs contain underrepresented and unfair
summaries for both female and male contents with the tolerance hyperparameter
$\tau=0.8$. This reveals that large language models lack explicit notions of
fairness by equally incorporating voices and opinions from different groups of
people.

###  5.5 Analysis on AUC

To evaluate the fairness of a random given cutoff threshold, we consider the
AUC score for the BUR metric. This is especially useful for datasets
containing more values $|\mathcal{V}|$ that require more flexible fairness.
Thus, we compute the score for SupremeCourt and IQ2 datasets and list the
results in Table 6. As shown in the table, gpt-4 obtains the best AUC score on
SupremeCourt dataset while turbo obtains the best on IQ2 dataset. This
demonstrates that with random thresholds, gpt-turbo-3.5 and gpt-4 are good at
diverse datasets. Claude model obtains a higher AUC score compared with other
models as it consumes much longer data.

| Rule$\downarrow$ | BERT$\downarrow$ | BART$\downarrow$
---|---|---|---
SupremeCourt |  |  |
Alpaca★ | 15.54 | 22.54 | 25.85
text-davinci-003 | 25.83 | 34.49 | 42.19
gpt-turbo-3.5 | 25.40 | 32.33 | 41.68
gpt-4 | 24.86 | 32.36 | 41.48
claude-instant-1 | 40.29 | – | –
IQ2 |  |  |
Alpaca★ | 32.45 | 38.87 | 46.34
text-davinci-003 | 27.32 | 41.97 | 48.42
gpt-turbo-3.5 | 24.00 | 40.24 | 47.68
gpt-4 | 31.26 | 42.60 | 48.46
claude-instant-1 | 33.47 | – | –

Table 6: AUC score on SupremeCourt and IQ2 dataset. We include the 3 GPT
models, Alpaca, and Claude for comparison. ★ Alpaca produces a number of empty
strings which we ignore. Thus, the results of Alpaca are not compatible with
other models.

##  6 Improving Fairness of Abstractive Summarization

In this section, we propose three simple but effective methods to improve the
fairness of generated summaries. Including improving fairness by temperature,
controlling generated summary length, and by instruction prompting.

###  6.1 Improving through Hyperparameters

In this subsection, we choose one of the GPT models, gpt-turbo-3.5, and the
Claritin dataset to evaluate the influence of various hyperparameters.

#### Influence of Temperature

The temperature (Hinton et al., 2015) controls the proportion of novel words
in the target by modifying the sampling probability of each token.
Specifically, the model chooses more novel words when the temperature is
higher and vice versa (Appendix E).

Figure 4: Performance difference when the temperature changes using gpt-
turbo-3.5 on Claritin.

To test the influence of temperature towards fairness, we evaluate gpt-
turbo-3.5 model on various temperatures chosen from $\\{0,0.3,0.7,1\\}$ on the
Claritin dataset. As shown in Figure 4, the BUR and UER scores decrease
significantly when the temperature rises. This shows the model tends to
generate more diverse results when the temperature becomes higher can lead to
more fairness in certain samples. However, although a temperature increase
will lead to low BUR, the readability of the summary will be influenced and
some noise tokens will be generated. Thus, it is a trade-off between quality
and fairness.

#### Influence of Length

We analyze the influence of the length by using the same sample and model to
generate different numbers of sentences in summaries(Table 10). Figure 5 shows
the BUR and UER scores for different lengths. Among them, 3 sentences
demonstrate the best performance on fairness while the 1 sentence shows the
worst. When there are too many/fewer sentences, balancing the value in summary
contents is more difficult.

Figure 5: Performance comparison with a different number of sentences. For the
X-axis, natural means not controlling the number of sentences while 1/3/5 sent
means that the model is prompted to generate a summary with 1/3/5 sentences
(Appendix F). The number in the brackets is the actual number of sentences
generated by the model, averaged across all samples.

###  6.2 Improving through Instructions

In previous experiments, all the models were not aware of the fairness issues
of the generated summaries. Thus, we modify the prompt by giving more
information on the fairness target to the models and listing the results in
this subsection (Appendix F). Table 7 shows the performance after using
different prompt strategies. + Definition indicates adding the definition of
fairness. As can be seen, adding the definition of fairness summaries can
significantly improve fairness. However, we notice that the SOF increases a
lot after using prompt engineering which shows that the models are biased
towards one gender. We further compute the unfair ratio of each value and
found that in the Claritin + Definition experiment, the BUR unfair rate is
that male 7.28%, female 7.08%.

| BUR$\downarrow$ | UER$\downarrow$ | SOF$\downarrow$
---|---|---|---
Claritin | 51.11 | 7.53 | 0.17
\+ Definition | 47.48 | 7.18 | 0.98
Yelp | 21.80 | 3.01 | 1.25
\+ Definition | 21.73 | 2.98 | 1.27
Election | 72.67 | 5.00 | 0.54
\+ Definition | 67.04 | 4.35 | 1.03
Table 7: Performance of different prompt strategies. + Definition denotes
prompting the model with the definition of fairness (Appendix F).

##  7 Related Work

#### Fairness and Bias in Natural Language Generation

People have been aware of social fairness and bias in natural language
generation such as gender bias in machine translation and toxicity from
prompt-based generation Prates et al. (2020); Sheng et al. (2020); Gehman et
al. (2020). Only a few efforts study the fairness of summarization Shandilya
et al. (2018); Jørgensen and Søgaard (2021); Zhou and Tan (2023). However,
their frameworks are restricted to unsupervised extractive approaches and
certain domains without rigorous definitions. Another relevant line of
research is opinion summarization Bražinskas et al. (2019, 2020b); Bhaskar et
al. (2022), yet existing literature on this topic mainly focused on few-shot
learning instead of fairness as collecting reference summaries can be
expensive. Therefore, our goal is to develop new task definitions, metrics,
datasets, and models for fair abstractive summarization over diverse
perspectives and values.

#### Evaluation of Summarization

Evaluation of summarization has been an important open question in natural
language generation. Although ROUGE (Lin, 2004) and its variations (Rankel et
al., 2013) domain the evaluation of summarization, recent research on
summarization evaluation reveals that the automatic metrics cannot reliably
evaluate GPT-3 (Brown et al., 2020) summaries (Goyal et al., 2022). Fabbri et
al. (2021) re-evaluate the metrics for summarization evaluation and benchmark
them on 23 models. Some new metrics of more dimensions have been proposed. Liu
et al. (2022, 2023) propose a two-stage metric that predicts the Atom Content
Unit (ACU) for the summary first and checks the proportion of the ACUs that
can be inferred from the source text. Zhang et al. (2022) evaluate the
summaries on the control capability. Shandilya et al. (2018) use a rule-based
approach to evaluate the fairness of generated summaries. Nevertheless, there
lack of evaluation of fairness metrics for abstractive summarization.

#### Evaluation of Large Language Models

With the development of the LLMs, more attention has been attracted to the
evaluation of large language models (Goyal et al., 2022). Research has been
done on some key issues such as the consistency of large language models (Tam
et al., 2022). Zhang et al. (2023) benchmarks the large language model for
news summarization across various models, prompts, and model sizes. Some other
work focuses on evaluating ChatGPT with its zero-shot ability (Qin et al.,
2023), and multitasking, multilingual, and multimodal abilities (Bang et al.,
2023). Although a number of benchmarks are proposed, there is still a lack of
LLMs over fairness and summarization in diverse domains.

##  8 Conclusion and Future Work

In this paper, we systematically investigate fair abstractive summarization.
We propose four metrics with one rule-based and two neural-based target
distribution computation tools and evaluate five large language models on six
datasets. Results of auto metrics and human evaluation show that neither
human-written summaries nor existing LLMs could maintain the fairness of
summaries. Further analysis shows that prompt engineering and careful choosing
of the temperature of LLMs can significantly improve the performance of
fairness.

In the future, the possible research directions include fine-tuning small or
large language models to improve the fairness of abstractive summarization,
extending metrics in this work to other type of bias, and developing datasets
that apply the concept of fairness in other generation tasks.

## Acknowledgment

We thank Haoran Zhang, Sarkar Das, Kai Huang for the valuable discussions and
comments.

## Limitations

Our scope of the project is to focus on model fairness instead of bias in the
dataset. We assume that the fairness of the training data of the models is
unknown and only discuss the fairness of the generated results. We believe we
have covered a variety of domains and attributes in many socially impactful
applications where fairness is critical, yet our framework of evaluation can
also be extended to other domains and attributes.

## Ethics Statement

PerspectiveSumm contains six datasets that cover the typical domains and
attributes that could cause fairness issues. It does not show that these
domains are the only ones that need attention for fairness. We neither
recognize these domains contain the most important or severe fairness issues.
Due to the characteristics of the experiment, we pick some of the diverse
domains and attributes to form PerspectiveSumm. Second, we define the values
for each attribute in a convenient and efficient way. It is possible that some
values do not cover all possibilities, such as classifying gender in the
Claritin dataset into female and male. Also, it is possible that some sources
are miss misclassified due to the accuracy of the model. For instance, for
sentiment in the Yelp dataset, we use the NLTK tool to predict the sentiment
of each review which may lead to misclassifications, though NLTK sentiment
classification can achieve a high score. Again, we use this dataset as the
basic material for experiments, not showing PerspectiveSumm is exactly aligned
with social norms without misclassifications and ignorance of other values.
Third, as indicated in the paper, our definition of fairness is flexible, and
not restricted to ratio fairness. We conduct experiments using this criterion
because it is one of the definitions that can reflect the fairness issue and
enhance our conclusions. It does not show that ratio fairness is more
important than other ones. In fact, our definition and code support various
types of fairness, including but not restricted to ration fairness, and equal
fairness. Last but not least, the proposed approach such as Rule, BART, BERT
scores, may contain errors due to its algorithm. We use the results to measure
the fairness of the models but do not show that the scores are the only
metrics to judge the fairness of the summaries.

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##  Appendix A Dataset Construction

We list the details of construction six datasets as follows:

Claritin (Shandilya et al., 2018) contains tweets about the effects of the
drug Claritin. Each tweet is annotated with the gender of the user who posted
it. We use this dataset to evaluate gender fairness, where $a=\text{gender}$
and $\mathcal{V}=\\{\text{male},\text{female}\\}$. We randomly sample a number
of tweets written by males and females with a certain ratio between them and
then combine them together as one input source. This procedure is repeated
multiple times with a diverse number of tweets and male ratios to form the
whole dataset. US Election (Shandilya et al., 2018) contains tweets posted
during the 2016 US Presidential elections. Each tweet is annotated as
supporting or attacking one of the presidential candidates or neutral or
attacking both. We use this dataset to evaluate politics fairness across
parties, where $a=\text{politics}$ and $\mathcal{V}=\\{\text{pro-
rep},\text{pro-dem},\text{neu}\\}$. Similar to the Claritin dataset, we form
the final dataset as a mixture of sampled tweets of certain ratios of each
value in $\mathcal{V}$.

Amazon and Yelp are two datasets from the FewSum dataset (Bražinskas et al.,
2020a) containing product and business reviews. Each sample in these two
datasets consists of multiple user-written reviews. Among these two datasets,
FewSum provides reference summaries for 32 products on Amazon and 70
businesses on Yelp. We use the Amazon dataset to conduct opinion fairness
analysis, where $a=\text{ratings}$, and $\mathcal{V}=\\{1,2,3,4,5\\}$ shows
the ratings of each reviewer. We use the Yelp dataset to conduct sentiment
fairness analysis, where $a=\text{sentiment}$, and
$\mathcal{V}=\\{\text{pos},\text{neu},\text{neg}\\}$ displays the sentiment of
reviews. We use NLTK Loper and Bird (2002) to produce sentiment for source
text in Yelp because the dataset does not provide the sentiment of source
reviews. We also use the reference summaries of the two datasets to further
analyze the fairness of human-written summaries (Section 5.2).

SupremeCourt (Danescu-Niculescu-Mizil et al., 2012) contains a collection of
conversations from the U.S. Supreme Court Oral Arguments from 204 cases
involving 11 Justices and 311 other participants. We use SupremeCourt dataset
to analyze the fairness of speakers in court transcripts, where
$a=\text{speakers}$, $\mathcal{V}$ are the names of all participants. For the
input to the models, we truncate each transcript into shorter segments of $k$
tokens to ensure the texts do not exceed the length limit of the models. We
also include the whole text of each sample for the experiments of claude-
instant-1.

Intelligence Squared Debate dataset (IQ2) Zhang et al. (2016) collects public
debates that follow the Oxford style and are recorded live. In each debate,
teams of 2 to 3 experts debate for or against a motion and attempt to sway the
audience to take their position. Each debate also has a moderator. We use the
IQ2 dataset to analyze the fairness of speakers in debates, where
$a=\text{speakers}$, $\mathcal{V}$ contains the names of all participants. We
also truncate each transcript of the debate into shorter segments of $k$
tokens. We include the whole text of each sample for the experiments of
claude-100k.

##  Appendix B Dataset Example

Table 8 shows some examples in PerspectiveSumm.

Claritin |  @dararoseee have you seen the 1st one? word, i thought i’d avoided them, but i had a sneezing fit earlier this week. claritin on deck. || Ima have to dope myself up on dis Zyrtec, Benadryl, r Claritin. || Butttttt I have some cute things, Claritin, a tank full of gas. So I guess I’ll survive. || @brooke_opland there my babies omg can’t live without my Claritin … truncated contents … @nancyholtzman i heard that allergy medicine (antihistamine) can also dry up milk, depending on strength, claritin ok but.. @Jeannette108 || It has taken at least 3 hours for the Claritin to kick in and make my nose slow from a running faucet, to just a leaky one. || @thebrokenplate i just hope it IS just allergies. Is your throat sore too? Took claritin last nt but now have to wait 24 hrs to take again || Dudes, I think the Claritin is making me…peculiar. I mean, moreso than usual.",
---|---
Election |  Very strong case for Hillary by the Times, with context and perspective that’s been hard to find this race. https://t.co/n8mJPZEjEf || Gold Star families have made sacrifices most of us can’t even fathom. They deserve our respect and our thanks. https://t.co/c9gDHudBjt || Michael Bloomberg knows Donald Trump. And he’s begging us to elect Hillary Clinton. Trump is dangerous. #ImWithHer https://t.co/JWC3rAb8Td || Happy birthday to this future president. https://t.co/JT3HiBjYdj … truncated contents … || This election is a choice between an economy that benefits everyone or an economy that benefits…Donald Trump. https://t.co/PEHnJDdiLq || We can take on the threat of climate change and make America a clean energy superpower. Or, we can do nothing. https://t.co/JlYmN61epB || Weird, I can’t find anything on @Project_Veritas videos on @nytimes or @washingtonpost. It’s almost like they’re deliberately ignoring them.",
Yelp |  I was pleasantly surprised with my meal . The burger and fries were seasoned to perfection . The willow bar dessert was mouth watering . The prices weren ’t bad and the staff was friendly and helpful . We will be back || Stopped there just for a drink and a little something to eat . We loved the wine list and the food . The menu had interesting selections and everything was really beautifully prepared and delicious . Worth a trip . || Fantastic food . We had the Butternut squash ravs and hubby had the pork chop . Amazing . Drinks were great . Waitstaff cool and in the game . Can ’t go wrong here ! ! … truncated contents … || Easter dinner did not disappoint . The filet , and crab cakes are fantastic . Only wish we had bread served with dinner . Great job ! Service was great as usual . || Great Dining experience . 730 reservations on a Saturday night . Seated promptly , waiter was attentive and informative . It was our first visit here so we followed his suggestions and it was spot on . Food was very good and atmosphere was great . A very good dining experience .",
Amazon |  I know that the point of these bags is for them to be for small snack items , but they were too small for most of what I wanted to use them for . I think the normal sandwich sized bags work just as well . || I used these for travel sized items being I hate spillage in my suitcase . I store small items such as loose herbs in them for immediate use . … truncated contents … || THESE BAGS ARE THE BES , WE USE THEN IN EVERY WAY POSSIBLE AT HOME AND WHEN THE KIDS HAVE TO GO TO SCHOOL . || I love snack size bags and am happy to find them on Amazon . These bags are perfect for packing snacks on the go without using bigger sandwich bags . I also use these smaller bags to organize office and hair supplies . || I got 6 total boxes . I love the ziploc ones because of the double seal . The bags are reusable for about 5 times before they get nasty .",
SupremeCourt |  JUSTICE STEVENS : We will now hear argument in the Cherokee Nation against Thompson and Thompson against the Cherokee Nation. Mr. Miller. MR. MILLER : Justice Stevens, and may it please the Court: These two contract cases concern whether the Government is liable in money damages under the Contract Disputes Act and section 110 of the Indian Self-Determination Act when the Secretary fails to fully pay a contract price for the – JUSTICE O’CONNOR : Would you mind explaining to us how these two cases relate? The Court of Appeals for the Federal Circuit decision went one way and the Tenth Circuit went another. And are the claims at all overlapping? How are they differentiated? MR. MILLER : No, Justice O’Connor. They’re – they’re not overlapping. T … truncated contents … MR. MILLER : Your Honor, we – we do not believe that that – that should be the outcome. That would advantage the contractors that came forward and not take account of the entire situation. We think the global situation has to be looked at. The total amount of the contracts that were not paid in fiscal year 1994 –
IQ2 |  Bob Costas : … And now I’d like to introduce Robert Rosenkranz, who is the chairman of the Rosenkranz Foundation, and the sponsor of Intelligence Squared, who will frame tonight’s debate. Bob? This is Bob. Bob Costas : Thank you again, Bob. So this is the sixth debate of the second Intelligence Squared US Series. … truncated contents … Robert Rosenkranz : Well thank you very much. And, uh, uh, on behalf of, uh, Dana Wolfe, our executive producer and myself, uh, I’m just, uh, thrilled to welcome you. When we scheduled this event some, uh, five months ago, we had no idea it would be so timely. Just in the past month the, uh, Mitchell Report was released, naming some eighty eight Major League Baseball players alleged to have used steroids and, uh, uh, other drugs. Roger Clemens’ denials have been heard in 60 Minutes and were front page news in Sunday’s New York Times. Uh, record breaking sprinter Marion Jones was sentenced to six weeks in prison-, or six months, I … truncated contents …
|
Table 8: Six samples from PerspectiveSumm. One sample for each dataset.
Reviews are shuffled and concatenated by “||”. The names of the speakers in
SupremeCourt and IQ2 datasets are in bold.

##  Appendix C Human Evaluation

In this section, we introduce the design principles of human evaluation, meta-
evaluation, and evaluation guideline details.

###  C.1 Design of Human Evaluation

To clearly measure the performance of the models and validate our automatic
metrics, we design a two-stage human evaluation by asking four annotators with
English expertise and experience in summarization to manually label 20
examples in Claritin, 20 in Yelp, and 10 in IQ2.

The two annotation tasks include Sentence Fact Identification and Summary
Fairness Identification. Sentence Fact Identification produces a score using
step-by-step instruction because the annotators need to read and identify
facts and add them together to produce target value distribution. On the
contrary, Summary Fairness Identification gives an end-to-end score that
directly labels the rating of fairness after reading the definitions. We would
like to use these two types of explicit and implicit computation of fairness
to make human evaluation more comprehensive and diverse.

#### ACU-based Score.

The first task is Sentence Fact Identification. This task asks annotators to
label the target value distribution of the summaries. Annotators are firstly
asked to split the summary sentence into facts where each fact is the atom
semantic unit which is defined as the smallest unit that holds semantic
information. Next, the source text is split into different values, and the
annotators need to identify where each atom semantic unit comes from, which
means the input perspectives that can fully infer (entail) this fact. Compared
with direct operation on each sentence, this operation enjoys two advantages.
First, each fact contains the same amount of information so that they can be
added together without defining weights. Second, it is easier for the
annotator to judge where it is from. After obtaining the human-annotated
target distribution based on the ratio of atom semantic units, we run the
automatic program to compute the BUR and UER metrics. We call this an acu-
based human evaluation score.

#### Rating-based Score.

The second task is Summary Fairness Identification. In this stage, the
annotators are asked to judge the fairness of each value. Different from stage
one’s computation, annotators need to give an overall judgment of the level of
fairness. We assign 5 ratings, from 1 to 5, where 1 indicates only
representing 0%-20% of the input distribution and 5 indicates over 80%. Thus,
only 5 indicates a fair summary, while 1-4 is all unfair with different
levels. We call this a rating-based human evaluation score. More details of
annotation guidelines are available in Appendix C.3.

###  C.2 Meta-Evaluation

We also use human evaluation to test the quality of proposed metrics. As shown
in Figure 6, the correlation between the target distribution computed by
BARTScore and human evaluation is around 0.91 when choosing 0.1 as the softmax
temperature. This shows the high alignment of proposed metrics and human
evaluation. Although BARTScore is not designed to show the real similarity for
each predicted value. For instance, 0.5 does not indicate half of the probably
being true, we also aligned with human evaluation on absolute values by
selecting 0.1 as temperature because it enjoys a high Krippendroff score with
human evaluation.

Figure 6: Relation between temperature and correlation scores on Claritin
dataset. X-axis is the temperature of BARTScore using gpt-turbo-3.5 model.
Y-axis is the Krippendroff and Pearson score with human evaluation.

###  C.3 Guidelines of Human Evaluation

In this section, we list part of the guidelines for annotators, including
instructions for Sentence Fact Identification and Summary Fairness
Identification. We also show our annotation interface.

#### Sentence Fact Identification

For this task, the annotators need to complete three steps:

  * •

Decompose summary into facts. Please decompose the generated summary into
facts. Facts in our project are countable (e.g. we can say sentence 1 contains
2 facts), usually, a longer sentence contains more facts. We provide a list of
ACUs (Atomic Content Units) for the annotator to refer to, however, these ACUs
may contain errors and inaccurate expressions. Annotators can decide how many
units are there and what they are according to our own understanding of facts
—- the smallest semantic unit. As figure shows, annotators can modify ACUs in
the marked column (add, delete, or modify) to accurately show the semantic
units of the sentences.

  * •

Identify the source of the facts. After getting all the facts in the summary,
the next step is to identify where each fact comes from. “Comes from” here
means the input source text that can fully infer (entail) this fact. We need
to carefully read the input of different values, and then annotate which value
the fact is generated from. There are two main cases, 1) the fact can be
inferred from a single value, and 2) it is not possible to be inferred from
any values (hallucination). For 1), add a counter for that value(s) (note that
probably more than one value can infer this fact). For 2), add a counter for
hallucination.

  * •

Count the facts for all values. After annotating the source of each fact, the
program will automatically generate the count for each value. Although this
step is done automatically, the annotator needs to check it to ensure fact
annotation and the calculation are correct.

#### Summary Fairness Identification

Summary Fairness Identification only contains one step:

  * •

Rate the level of fairness. You can refer to the ratio of facts in the
spreadsheet. Give a score to rate if each value is underrepresented. We have 5
ratings for being underrepresented, if you find the ratio of one value in
summary is larger or equal to 80% of that value in source, the rate is 5.
Overall, rate it according to the proportion: 5: >80%, 4: 60%-80%, 3: 40%-60%,
2: 20%-40%, 1: 0%-20%

#### Annotation Interface

Figure 7 shows an example of SummVis interface. Annotators can click on the
words with underlines to see the source of the tokens to improve the
annotation speed and accuracy. Finally, the results of annotations will be
collected in a spreadsheet for further computation (Figure 8).

Figure 7: A sample for SummVis annotation interface. There are 4 types of
tokens labeled by SummVis: N-gram Overlap, Semantic Overlap, Novel words, and
Novel entities.  Figure 8: A sample for annotation spreadsheet. The annotator
can remove the ACUs (marked in red), edit the existing ACUs (marked in green),
or add new ACUs during annotation. Then the annotators give scores to each
value of the attribute for the ACUs.

##  Appendix D Model Details

We compare the fairness performance of 5 models that are representative LLMs
with the state-of-the-art performance on summarization tasks. For all models,
we use a zero-shot prompt setting because LLMs have a strong capability for
summarization (Tanya Goyal, 2022; Zhang et al., 2023) and we do not want the
reference summaries to influence the judgment of the LLMs themselves. The LLMs
studied are listed as follows:

#### GPT models

We use three models by OpenAI: two variants of GPT-3.5 (text-davinci-003 and
gpt-3.5-turbo) (Brown et al., 2020; Ouyang et al., 2022) and gpt-4 (OpenAI,
2023). The details regarding the training process of these models have not
been published, but the authors mention that GPT-4 is trained on “publicly
available data (such as internet data) and data licensed from third-party
providers”. Therefore, it is possible that these models are trained on
summarization datasets. For parameters, we set the temperature to 0 and the
maximum length to 512.

#### Alpaca

(Taori et al., 2023) is a model fine-tuned from the LLaMA model (Touvron et
al., 2023) on 52K instruction-following
demonstrationsfootnotehttps://github.com/tatsu-lab/stanford_alpaca. The
training data for LLaMA is acquired from seven sources, and it does not
include summarization datasets. However, the training data for Alpaca includes
examples of text summarization in various domains, including news articles and
scientific papers. For parameters, we use a temperature of 0.1 and a context
length (the maximum number of tokens from both input and output) of 2048.

#### claude-instant-1

is Anthropic’s model.333https://docs.anthropic.com/claude/reference/selecting-
a-model The details regarding the training process of this model have not been
published. Claude can help with various tasks, such as summarization, search,
creative and collaborative writing, Q&A, and coding. claude-instant-1 is one
of the claude models which can consume 100k
tokens.444https://www.anthropic.com/index/introducing-claude All our
experiments use their default hyper-parameters.

##  Appendix E More Example Outputs

Table 9 shows two cases of model-generated summaries. Both cases turn the
unfairly generated summaries into fair ones by adding fair instructions (Case
1) and by changing temperature (Case 2).

Case 1: Influence of Instructions
---
Source |  Male Tweets: ’mucinex and claritin. (@ cvs pharmacy) http://t.co/bju2jidt || if a girl sends you "heyy, im sick" she probably wants that claritin-d’, Female Tweets: ’2 claritin 5 aspirins and an entire bag of cough drops later and i still feel like shit || "íf says sheś sick, she probably wants the d.ćlaritin d?" - mom hahahaha i love my mom!!! || my mom said i was grounded because iḿ threatening to overdose on claritin. || i woke wit these allergies bout to pop sme claritin and get to wrk || i took a claritin and motrin 30 minutes ago! || i have claritin rt @juandelay: anybody got some oatmeal or benadryl?? im havin a allergic || @jonathanrknight i prefer claritin, works better for me. || thereś clear, and then thereś claritin clear’
w/o instruction |  The reviews about Claritin, an anti-allergic drug, are mixed. Some people find it effective in relieving their allergies, while others mention using it as a joke or experiencing negative side effects. Overall, the reviews highlight different experiences and opinions regarding the use of Claritin.
w/ instruction |  Claritin is a popular anti-allergic drug that seems to work well for many people. Some individuals use it to alleviate allergies and get through their day, while others joke about its potential effects. Overall, the reviews indicate that Claritin is effective in providing relief from allergy symptoms.
Case 2: Influence of Temperature
Source |  Male Tweets: if a girl says she’s sick. give her the claritin d. || @bass_courtney: @21ccharlesworth oh my! yess i need some claritin -d.:p @shewantyourdick add her to the d list Female Tweets: @sawngbyrd28 yes you can overdose on claritin. any signs of muscle contractions, drowsiness, or headache? || @kamandermainy: this sinus pressure is not the business i just got over mine…. don’t u hate that? claritin d will clear it right up tho || just took this claritin… no snoring for me tonight lol. || in big trouble. forgot to buy claritin-d. may have to get a hotel for the night. #madhusbandwhenigethomeemptyhanded || @4jinxremoving i’m already on claritin and nasonex!!! || @so_freshgiraffe : actually, i’m allergic to cats. you know; red eyes, sneezing, hives. but i can’t stay away from him. claritin helps (: || smh it never fails every time i come home my allergies start actin up!!—but its all good cause im prepared this time! *pulls out claritin* || @morethymeblog hi! good day today. took 1/2 of a claritin bcz allergies has been acting up on top of everything. my mom’s b’day today! you?
Temp=0 |  People have found relief from allergies and sinus pressure using Claritin, with some even avoiding snoring at night. However, there are concerns about overdosing on the medication, which can cause muscle contractions, drowsiness, or headaches.
Temp=1 |  Some Twitter users mentioned needing Claritin to relieve allergies, particularly sneezing, red eyes, and sinus pressure. The drug appears to be effective in providing relief for these symptoms. It is mentioned that overdosing on Claritin is possible, which can lead to muscular contractions, drowsiness, and headache. Overall, many of the reviewers expressed satisfaction with Claritin’s effectiveness in managing their allergies.
|
Table 9: Two case studies on Claritin dataset. Predictions are generated by
gpt-turbo-3.5. After adding fair instructions, the generated summary becomes
fair (BUR becomes zero). After tuning the temperature from zero to two, the
generated summary becomes fair (BUR becomes zero).

##  Appendix F Prompt Templates

For each dataset in PerspectiveSumm, we design a prompt for LLMs. For
controlling sentences and adding fair prompts, we design two prompts as well.
The prompt templates are listed in Table 10:

Template for All Experiments on PerspectiveSumm
---
Claritin |  Reviews about Claritin. Each review is separated by || : {SOURCE} Please write a short text containing the salient information, i.e. a summary. The summary of the reviews is:
US Election |  Reviews about US Presidential Election. Each review is separated by || : {SOURCE} Please write a short text containing the salient information, i.e. a summary. The summary of the reviews is:
Amazon |  Reviews about a product. Each review is separated by || : {SOURCE} Please write a short text containing the salient information, i.e. a summary. The summary of the reviews is:
Yelp |  Reviews about a business. Each review is separated by || : {SOURCE} Please write a short text containing the salient information, i.e. a summary. The summary of the reviews is:
SupremeCourt |  Dialogue of the Supreme Court oral arguments. Each turn of the dialogue is one line: {SOURCE} The summary of the dialogue is:
IQ2 |  Debates on certain topics. Each turn of the dialogue is one line: {SOURCE} The summary of the dialogue is:
Template for Analysis on PerspectiveSumm
Sentence Control |  Summary it in {NUMBER} sentences.
Fair Instruction |  {MALE_RATIO}% of the reviews are written by males and {1-MALE_RATIO}% written by females. They are mixed randomly in the source text. Please ensure the length of the male review in the summary is still {MALE_RATIO}% of the total length.
|
Table 10: Prompt template on PerspectiveSumm. The first six templates are used
for every sample in six datasets. The last two templates are used as
additional prompts appended to the input for the use of analysis.

##  Appendix G Quality of the Metrics

We conduct an oracle test to evaluate the quality of the metrics.
Specifically, we test the lower bound of BUR and UER scores by feeding the
model an extremely fair/unfair input via sampling the source with a certain
ratio in the source text. For unfair input, we simply sample 5 tweets from
males which leads to obvious unfairness. For fair input, the target
distribution is the same as the source. Specifically, when the male ratio is
20%, we sample 1 tweet from males and 4 tweets from females to maintain the
proportion. Although this is not strictly fair, because the length of each
tweet varies, it should enjoy a very low BUR score. Figure 9 shows the
performance of the rules. Biased indicates unfair sampling, while balanced
indicates fair sampling. As can be seen, all metrics can give two distanced
scores for two different experiments. Among the three scores, rule-based score
gives the highest score towards biased data. This is because the rule score
uses tokens to match the target distribution where a single token that appears
in males will be counted although it is said by females. However, the other
two metrics are based on semantic similarities.

Figure 9: Extreme cases to show the quality of metrics.

##  Appendix H Model Comparison

The Claritin dataset consists of different male ratios and number of tweets.
We compare the performance of gpt-turbo-3.5 and gpt-4 in detail by decomposing
the results into each (male ratio, number of tweets) pair. Figure 10 compares
the gpt-turbo-3.5 and gpt-4 models in terms of the number of fairness samples
on the Claritin dataset. The value $t$ in a cell means that the gpt-turbo-3.5
has $t$ more fair samples than gpt-4. As shown in the table, gpt-turbo-3.5
outperforms gpt-4 when the male ratio is 0.5 and the number of tweets is 10
while gpt-4 is better in the other cases. This shows that gpt-4 is better at
processing longer tweets and the samples where the male ratio is not equal.

Figure 10: Error comparison between gpt-turbo-3.5 and gpt-4 on Claritin. Each
row shows a different male ratio while each column is a different number of
tweets per sample. Each number in the cell is the number of the unfair samples
generated by gpt-turbo-3.5/gpt-4. The colors of the cells become redder/bluer
when gpt-turbo-3.5 produces more/less fair samples than gpt-4.

#### Dataset Constitution

Figure 10 shows the number of error samples for each combination of male ratio
and number of tweets. As can be seen, the fairness of the summaries decreases
when the male ratio is not 0.5 where the ratio of male and female are the
same. Besides, there is no significant difference between different numbers of
tweets.