# On the Ethical Considerations of Text Simplification

Sian Gooding
Department of Computer Science and Technology
University of Cambridge
<EMAIL_ADDRESS>
###### Abstract

This paper outlines the ethical implications of text simplification within the
framework of assistive systems. We argue that a distinction should be made
between the technologies that perform text simplification and the realisation
of these in assistive technologies. When using the latter as a motivation for
research, it is important that the subsequent ethical implications be
carefully considered. We provide guidelines for the framing of text
simplification independently of assistive systems, as well as suggesting
directions for future research and discussion based on the concerns raised.

##  1 Introduction

Assistive technology refers to the devices used to support or aid those living
with disabilities Preston (2003). The intent behind such technologies is to
increase independence and maximise societal participation for individuals Borg
et al. (2011).

There are many examples of assistive technology that rely on speech and
natural language processing. For instance, sign language translation Camgoz et
al. (2018), pronunciation adaptation for disordered speech Sriranjani et al.
(2015) and synthesised voices for individuals with vocal disabilities Veaux et
al. (2013). Text simplification is an area of natural language processing
concerned with the simplification of textual information and is often
recognised as having assistive applications. Prior research in text
simplification posits that such technology may be beneficial for audiences
with reading difficulties or a range of disabilities such as dyslexia, aphasia
or deafness.

However, currently the algorithms designed for text simplification are
considered in isolation from their assistive applications, and there is
subsequently little discussion on the ethical implications for the intended
users. Text simplification research is often motivated by highlighting the
audiences that could benefit from such tools, thereby coupling the technology
with the assistive applications. Framing text simplification via the
implications for assistive technology means that the ethical considerations
cannot be easily separated from the technology used to generate the result. An
issue which is commonly acknowledged in the assistive technology literature
Niemeijer et al. (2010).

There are many potential benefits of text simplification embedded in assistive
technology, and both for service providers and service users, there are also a
number of ethical issues that must be considered. In this paper, we will
discuss the ethical considerations that arise from the embedding of text
simplification within assistive technologies. Our aim is to encourage the
discussion and consideration of these issues, as well as inform the design
decisions of future assistive technologies that incorporate text
simplification.

##  2 Background

| Some philatelists say the committee that helps the
---
postmaster general pick new stamps is favoring pop
celebrities and fictional characters over cultural sites
and historical figures, undermining a long tradition.
$\downarrow$
| Some philatelists (as stamp collectors are known) say
---
the committee that helps pick new stamps is favoring
pop stars and fictional characters. Such choices mean
that cultural sites and historical figures are appearing
less often. They say this results in the undermining of
a long tradition.
Table 1: Example of manually simplified sentence from the Newsela Dataset Xu
et al. (2015)

Complete textual simplification requires many types of transformations which
can be grouped into three categories: syntactic, lexical and conceptual
Siddharthan (2014). Table 1 illustrates a range of simplification operations
from these different categories, a description of these is as follows:

Lexical simplification is concerned with reducing the complexity of words
within a text Paetzold and Specia (2017); Gooding and Kochmar (2019). In
lexical simplification, complex words are identified and replaced with simpler
alternatives. We observe an example of lexical simplification with the case of
celebrities being simplified to stars.

Syntactic simplification aims to reduce the grammatical complexity of text by
simplifying the syntactical structures. Examples of such transformations
include the conversion of text from passive to active voice and dis-embedding
relative clauses Siddharthan (2006a). In our example, multiple syntactic
simplifications have taken place. One such simplification occurs where the
subordinated clause ‘…undermining a long tradition’ has been split into a
separate sentence. Syntactic simplification often requires discourse
preserving edits to maintain the coherence and cohesion of simplified text.
For instance, the addition of ‘They say…’ is necessary to convert the original
relative clause into a grammatically correct and coherent sentence.

Finally, conceptual simplification focuses on the simplification of ideas or
concepts within text. The example shows how the concept of philatelist has
been simplified by providing an explanation of the term. This simplification
technique is commonly referred to as elaboration, as the meaning of the
concept has been elaborated on Siddharthan (2006a). Often, this strategy is
used in cases where no alternative synonym would suffice, for instance with
named entities.

Both syntactic and conceptual simplification contain parallels with the
research area of text summarization as omitting peripheral or inappropriate
information, as well as distilling complex concepts, is relevant for both.
However, in simplification these processes can increase the length of the
original text, whereas in summarization the goal is to constrain the length of
the resulting summary.

In automatic text simplification, the aim is to transform text using the
aforementioned operations, to allow individuals with differing comprehension
levels access. This requires a fundamental understanding of what factors
contribute to text complexity for differing audiences Gooding et al. (2021b).

Early approaches to automated simplification were largely rule-based systems
Canning et al. (2000); Carroll et al. (1998a); Siddharthan (2006b), with many
prioritising syntactic operations, such as sentence splitting, deletion or
reordering. However, some work combined lexical simplification with syntactic
operations Coster and Kauchak (2011); Kauchak (2013); Zhu et al. (2010). In
recent years simplification has been viewed as a monolingual translation task
Kauchak (2013); Zhang and Lapata (2017); Zhu et al. (2010). These systems
perform a number of simplification operations at once by aiming to translate
complex English to simple English. Initial approaches attempt this with
phrase-based machine translation Coster and Kauchak (2011); Wubben et al.
(2012) while subsequent work has focused on neural machine translation
techniques Nisioi et al. (2017); Zhang and Lapata (2017); Shardlow and Nawaz
(2019); Dong et al. (2019).

##  3 Risks and Harms

In this section we outline and discuss the potential risks and harms that
arise from the integration of text simplification within assistive technology.

###  3.1 Intended Audience

Audience outline |  Datasets |  Evaluation |  Venue
---|---|---|---
(1) …such as children, people with low education, people who have reading disorders or dyslexia, and non-native speakers of the language. |  Newsela WikiLarge Biendata |  Automatic |  _ACL 2021_
(2) It provides reading assistance to children Kajiwara et al. (2013), non-native speakers Petersen and Ostendorf (2007); Pellow and Eskenazi (2014); Paetzold (2016) and people with reading disabilities Rello et al. (2013b) |  Newsela |  Automatic + 5 workers |  _NAACL 2021_
(3) It can provide convenience for non-native speakers Petersen and Ostendorf (2007); Glavaš and Štajner (2015); Paetzold and Specia (2016c); Rello et al. (2013b), non-expert readers Elhadad and Sutaria (2007); Siddharthan and Katsos (2010) and children De Belder and Moens (2010); Kajiwara et al. (2013) |  D-Wikipedia Newsela |  Automatic + 3 workers |  _EMNLP 2021_
(4) …to children De Belder and Moens (2010); Kajiwara et al. (2013), people with language disabilities like aphasia Carroll et al. (1998b, 1999b); Devlin and Unthank (2006), dyslexia Rello et al. (2013a, b), or autism Evans et al. (2014); non-native Petersen and Ostendorf (2007); Paetzold (2015); Paetzold and Specia (2016b); Pellow and Eskenazi (2014) English speakers, and people with low literacy skills or reading ages. |  WikiSmall WikiLarge |  Automatic |  _BEA 2021_
Table 2: Examples of paper introductions outlining audiences benefiting from
text simplification, alongside the evaluation techniques and venue, specific
paper references are included in Appendix A.

As with many areas of research, the field of text simplification has converged
on a partially boilerplate preamble outlining a set of motivations. Table 2
features extracts taken from a sample of recent text simplification papers.
These papers were sampled by searching the ACL anthology for the term text
simplification and ordering by most recent. We look specifically at sections
outlining the audiences said to benefit from text simplification as a whole.
Below, we consider the ethical implications of citing such audiences as a
motivation for text simplification.

####  3.1.1 The Homogeneity Effect

As shown in Table 2, the audiences stated to benefit from text simplification
are often listed together, namely non-native speakers, children, people with
low literacy skills, people with reading disabilities or disabilities
generally. Based on this, a reader may be given the impression that general
purpose text simplification works adequately for all of the stated groups.
Whereas in fact, there is evidence to show that text simplification may not be
effective for second language learners Young (1999), that alternative
strategies to simplification can be most effective for dyslexia Rello et al.
(2013a) and that automated text simplification cannot simplify content to a
low enough level for children De Belder and Moens (2010).

Framing the benefit of text simplification as net positive for all groups can
have consequences for the development of assistive technology, as merging the
audiences serves to diminish the sensitive differences in needs for these
groups. Even for specific audiences, such as children, there is a consensus
that the homogeneous grouping of reading ability can have detrimental outcomes
for learning Schumm et al. (2000).

A further complication, is that the references commonly used in support of
text simplification (for specific audiences) are often more nuanced than
stated. For instance, the work of Rello et al. (2013a) is commonly cited as
showing the benefit of text simplification for dyslexic readers. However, this
paper demonstrates that the most effective strategy to help dyslexic readers
with difficult words, is to provide a range of synonyms for the word, and not
to simplify the original. Furthermore, the work of Carroll et al. (1998a) and
Carroll et al. (1999a) is put forward as evidence for the utility of
simplification for individuals with aphasia. However, both of these works
outline the proposal for a simplification system targeted for aphasia and
propose to evaluate the effectiveness of such a system in future work. A final
example, used in support of text simplification for children is a paper by De
Belder and Moens (2010). However, the paper finds that even using lexical and
syntatic simplification, it was not possible to reduce the reading difficulty
enough for children.

####  3.1.2 Datasets and Evaluation

Text simplification has many subtleties, as what would be a valid
simplification for one reader may not be appropriate for another Xu et al.
(2015). For instance, it has been shown that the factors contributing to word
complexity vary depending on the first language and proficiency level of a
reader Gooding et al. (2021b). The subjective nature of text simplification
means that system evaluation is difficult. Furthermore, as there is not one
‘ground truth’ for simplification, the efficacy of automatic evaluation
measures is limited. Prior work on the development and evaluation of
simplification systems has given little consideration to the target reader
population Xu et al. (2015).

As exemplified in Table 2, current work on text simplification typically
relies on automatic evaluation, with the occasional use of human evaluation.
When considering the approach to human evaluation, most work does not specify
what “being simpler” entails, and trusts human judges to use their own
understanding of the concept Alva-Manchego et al. (2021). It is also currently
not standard practice to include the demographic information of the workers.
When using human judgements as a measure of simplification quality, it is
important to include relevant information on the demographic background, so
that valid conclusions can be drawn about which target population may benefit
from the system. Additionally, the concept of what constitutes adequate
simplification needs to be precise if the system is aimed for a specialised
audience.

The datasets commonly used to train text simplification systems (i.e. Newsela
and Simple Wikipedia) have drawbacks such as poor alignment, lack of
simplicity and not being tailored for a specific audiences Xu et al. (2015).
In text simplification, it is important to discuss the limitations of the data
so that the suitability of such systems for specialised groups is clearly
recognised.

In summary, when claiming the benefits of text simplification for specific
audiences, it is crucial that the needs of these groups are understood. This
is especially the case when emphasising the benefit of such technology for
disabled groups. The development of assistive technology is downstream from
research, and therefore being clear about the suitability and limitations of
the technology for differing audiences helps to avoid poorly suited assistive
technology solutions being developed.

###  3.2 Meaning Distortion

There are multiple genres of text where access is highly important, such as
healthcare information or political materials. The benefits of simplifying
such content have been shown, for example simplifying text in health care
improves understanding of information regardless of health literacy level Kim
and Kim (2015). Furthermore, the complexity of language matters for voters’
perceptions of political parties and their positions Bischof and Senninger
(2018).

The benefit of text simplification in such cases is apparent, as is the need
to ensure the meaning of such text is preserved and that no errors are
introduced. A drawback to current automated text simplification systems is
that the subtleties of meaning intended by the author may be diluted, if not
lost altogether Chandrasekar et al. (1996). For example, Shardlow and Nawaz
(2019) found that fully automated approaches omitted 30% of critical
information when used to simplify clinical texts. For these types of domains,
instead of fully-automated approaches, interactive text simplification tools
are better suited to generate more efficient and higher quality
simplifications Kloehn et al. (2018).

The link between factual correctness and natural language generation has been
considered for multiple domains such as summarization Cao et al. (2018), data
to document generation Wiseman et al. (2017) and dialog generation Shuster et
al. (2021). However, this is a relatively underexplored area for text
simplification and is currently not incorporated into the evaluation of such
systems.

Encouraging further discussion on this limitation of text simplification is
necessary. Especially when we consider the downstream applications of
assistive technology for critical consumer information.

###  3.3 Paternalism

There are choices made in the design characteristics of assistive technology
that can affect the degree of independence, privacy and participation that are
possible Lenker et al. (2013). These decisions have real world impact for the
users and thus warrant careful consideration.

The process of text simplification involves an understanding of what is
difficult and how best to simplify it. There are two approaches when deciding
what should be simplified. The first, involves including the reader in the
loop – either implicitly or explicitly. Relying on user signal to identify
areas for simplification has its own set of ethical concerns which are
discussed in $\S$ 3.4. The second approach, is performing general level
simplification with end-to-end systems. In fact, the majority of current work
in text simplification is now data-driven and performs simplification in a
‘black-box’ fashion Sikka and Mago (2020). One of the concerns for such
systems, is that they learn operations based on the simplification choices
made in the data they are trained with. As outlined in Section 3.1.2, most of
the data used to train text simplification systems is not audience specific Xu
et al. (2015).

Integrating general purpose simplification systems into assistive technologies
has a range of potential problems. For instance, it raises the issue of
“paternalism" which is the interference of a state or individual in relation
to another person Martin et al. (2007). The relationship between paternalism
and assistive technology is widely acknowledged, as design decisions made on
behalf of a user can be problematic if they override the autonomy of the
individual Martin et al. (2007, 2010). In the case of text simplification, not
allowing the individual the choice of what they would want simplified
restricts their autonomy.

Furthermore, assistive technologies should contribute to growth and
independence for individuals. The goals of text simplification are to make
textual information accessible to a range of different audiences. However, the
question of whether such systems should support learning is rarely discussed.
One concern with text simplification within assistive systems, is that it
would prevent the exposure to new terms and concepts thereby encouraging
learning stagnation.

In summary, the design decisions pertaining to what content is simplified have
ethical implications for the user. Removing the individual from the decision
process can reduce the person’s autonomy, and not allowing exposure to new and
unfamiliar terms limits learning opportunities, subsequently reducing the
user’s independence.

###  3.4 Privacy and Security

As outlined in Section 3.3, an effective approach for text simplification in
assistive technology is to include the user in the decision of what is
simplified. Prior research has shown that eye-tracking Berzak et al. (2018)
and scroll-based interactions Gooding et al. (2021a) correlate with text
understanding. As such, these implicit techniques can be used to gain an
insight into what the reader is finding difficult. The reader can also be
explicitly asked to select text that they would like to be simplified, for
instance by selecting words that are difficult for them Devlin and Unthank
(2006); Paetzold and Specia (2016a).

Adaptive text simplification is advantageous and provides autonomy and
learning opportunities for the user. However, the information about the areas
a user finds difficult is highly sensitive, and there is a responsibility to
ensure that such information is stored securely.

To protect the privacy of the user, the aim of the assistive technology and
the way it is used by service providers or care organisations must be clear.
Moreover, how personal data will be handled must be described explicitly in a
privacy statement and communicated to the user Martin et al. (2010).

This is a clear example of how viewing text simplification through the
paradigm of assistive technology yields more nuanced ethical considerations.
We believe it would be beneficial to encourage the discourse on such aspects
in the text simplification literature.

##  4 Going Forward

We suggest that papers focusing on general purpose text simplification should
de-couple the motivations from specific audiences with disabilities. An
example of a general purpose motivation by Nisioi et al. (2017) is as follows:

Automated text simplification (ATS) systems are meant to transform original
texts into different (simpler) variants which would be understood by wider
audiences and more successfully processed by various NLP tools.

Alternatively, if discussing the different groups of users who may benefit
from text simplification, being clear about the specific strategies that work
for these audiences is critical. Additionally, it is worth acknowledging that
when framing a system using a target demographic, it is appropriate that the
system is tested with that target group. For human evaluation generally, it is
highly beneficial to report the demographic statistics, as this allows an
insight into which types of audiences the system may work well for.

Finally, it is important to be forthright about the current limitations of
both the data and evaluation techniques used in automatic text simplification.
Whilst great improvements are being made in this area, these systems are still
far from perfect and this needs to be taken into account when judging the
suitability of systems for assistive technology.

##  5 Conclusion

Assistive technologies can dramatically affect the lives of those who rely on
them, and it is important to understand the potential ethical concerns –
especially as such technologies can impact vulnerable populations. In this
paper, we discuss a set of potential issues that arise from the embedding of
text simplification within assistive technologies.

Our aim in this work is to encourage further discussion on how the design
decisions of text simplification algorithms can have the potential to impact
future users of assistive technology.

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##  Appendix A Appendix

Paper references from Table 2:

  1. 1.

Explainable Prediction of Text Complexity: The Missing Preliminaries for Text
Simplification by Garbacea et al. (2021)

  2. 2.

by Controllable Text Simplification with Explicit Paraphrasing by Maddela et
al. (2021)

  3. 3.

Document-Level Text Simplification: Dataset, Criteria and Baseline by Sun et
al. (2021)

  4. 4.

Text Simplification by Tagging by Omelianchuk et al. (2021)