Text summarization is attracting more and more attention while deep neural network has had many successful application in NLP . One problem of such models is its inability to focus on the essentials of documents , thus generating summaries that may not be important , especially during multi-sentence summarization . The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance . To quickly have an overview of abundant information , Text Summarization ( TS ) comes into play . TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge . The hybrid model is depending on the linear combination of statistical measures like sentence position , TF-IDF , aggregate similarity , centroid , and sentiment analysis . Our idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message ; hence , it can play vital role in text document summarization . As we know for any sentence , emotions ( calling sentiments ) may be negative , positive , or neutral . In this paper we describe computational approaches to summarizing dynamically introduced information : online discussions and blogs , and their evaluations . Research in the past has been mainly focused on text-based summarization where the input data is predominantly newswire data . When branching into these newly emerged data types , we face number of difficulties that are discussed here . It is a tool used to display text data summarization in a visual way very easy to understand . However , it has not been extensively used in teaching , especially in elearning , where it would make a differential advantage . Sentence ranking is the key issue of text automatic summarization . Based on mutual reinforcement principles , we proposed a new sentence ranking model —— paragraph-sentence mutual reinforcement model . Among the possible uses it allows , it can be employed for communicating and broadcasting information in real time . The goal of this research is to analyze the task of automatic tweet generation from a text summarization perspective in the context of the journalism genre . To achieve this , different state-of-the-art summarizers are selected and employed for producing multi-lingual tweets in two languages ( English and Spanish ) . We present a work in progress report of our research on automatically analyzing German court decisions . For accessibility to information retrieval and text summarization processing , we show concepts of a parsing framework dealing with linguistic features of this genre . To cover these features , we first developed a description language inferred from content structure analysis presented before . Indeed , models that are able to assign a low probability to sentences that are grammatically correct but unlikely may help other tasks in fundamental language understanding like question answering , machine translation , or text summarization . LMs have played a key role in traditional NLP tasks such as speech recognition , machine translation , or text summarization . paragraph : Multi - source abstractive summarization based RC . The first idea is to use a pointer - generator mechanism for multi - passage RC , which was originally proposed for text summarization SeeLM17 . section : Datasets We consider three major WMT translation tasks as well as a text summarization task . WMT'16 document : Abstractive Text Summarization using Sequence - to - sequence RNNs and Beyond In this work , we model abstractive text summarization using Attentional Encoder - Decoder Recurrent Neural Networks , and show that they achieve state - of - the - art performance on two different corpora . We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture , such as modeling key - words , capturing the hierarchy of sentence - to - word structure , and emitting words that are rare or unseen at training time . document : Faithful to the Original : Fact Aware Neural Abstractive Summarization Unlike extractive summarization , abstractive summarization has to fuse different parts of the source text , which inclines to create fake facts . Our preliminary study reveals nearly 30 % of the outputs from a state - of - the - art neural summarization system suffer from this problem . We experimented in four settings : from English to German ( En - De ) , its reverse ( De - En ) , from English to French ( En - Fr ) , and its reverse ( Fr - En ) . Headline generation is a task that creates a short summarization of an input sentence . rush - chopra - weston:2015:EMNLP constructed a headline generation dataset by extracting pairs of first sentences of news articles and their headlines from the annotated English Gigaword corpus . We propose a collaborative approach to improve document summarization by incorporating social contextual information into the sentence ranking process . Both the relationships between sentences from document context and the preference information from user context are investigated in the approach . However , most existing summarizers are general purpose . Thus , they do not consider neither annotations nor user skills during the document summarization process . This paper studies the application of a state-of-the-art summarization system , namely , the itemset-based summarizer ( ItemSum ) , in an e-learning context . The sentences are picked using simple coverage and orthogonality criteria . We describe a novel combinatorial formulation that captures exactly the document-summarization problem , and we develop simple and efficient algorithms for solving it . We compare our algorithms with many popular document-summarization techniques via a broad set of experiments on real data . In recent years , algebraic methods , more precisely matrix decomposition approaches , have become a key tool for tackling document summarization problem . Typical algebraic methods used in multi-document summarization ( MDS ) vary from soft and hard clustering approaches to low-rank approximations . Based on these principles , a greedy yet very efficient entry selection algorithm is proposed . To evaluate the entry selection algorithms , an extrinsic evaluation methodology from document summarization research is adapted . Specifically , we evaluate the proposed entry selection algorithms by examining their blog classification accuracies . We develop a sentence level selective encoding mechanism to select important feature before extracting sentences , and use a novel reinforcement learning based training algorithm to extend the sequence model . Besides , for single document extractive summarization task , most of researchers only pay attention to the main part of document . We analyze and explore the side information such as the headline and image caption in both CNN and Daily Mail news datasets . Graph-based models and ranking algorithms have been drawn considerable attentions from the document summarization community in the recent years . However , in regard to query-oriented summarization , the influence of the query has been limited to the sentence nodes in the previous graph models . To solve the problem of automatic muti-document summarization with spasre topics , the recently proposed fully sparse topic model is introduced into the document summarization field . An automatic summarization algorithm based on topic distribution and words distribution is proposed . Several examples are illustrated in Table [ reference ] . NLI is in the core of natural language understanding and has wide applications in NLP , e.g. , question answering and automatic summarization . Moreover , NLI is also related to other tasks of sentence pair modeling , including paraphrase detection , relation recognition of discourse units , etc . Our fast architecture is interesting for character level encoders where the input is significantly longer than for words . Also , we plan to investigate the effectiveness of our architecture on other sequence - tosequence tasks , e.g. summarization , constituency parsing , dialog modeling . section : A Alignment Visualization They show promising results on their Chinese dataset using an encoder - decoder RNN , but do not report experiments on English corpora . In another very recent work , jianpeng used RNN based encoder - decoder for extractive summarization of documents . This model is not directly comparable to ours since their framework is extractive while ours and that of , and is abstractive . The vector encodes the ( neighboring ) node and the edge connected to , which can be viewed as a vector representation of the - th candidate action ( excluding the STOP action ) . And the vector is a vector summarization of and , which is used to model the STOP action probability . In summary , we use the sub - vectors to model and according to : where denotes inner product , is a fully - connected neural network with model parameter , denotes the element - wise sigmoid function , and is the softmax function with temperature parameter . Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods . In this paper , we propose the task of abstractive timeline summarization , which tends to concisely paraphrase the information in the time-stamped events . Automatic headline generation is a sub-task of oneline summarization with many reported applications . Evaluation of systems generating headlines is a very challenging and undeveloped area . Also , the bursts detection of these items are derived . The co-occurrence of keywords and their bursts detection and timeline review are shown , respectively . Finally , some conclusions are given . Since many applications such as timeline summaries and temporal IR involving temporal analysis rely on document timestamps , the task of automatic dating of documents has been increasingly important . Instead of using feature-based methods as conventional models , our method attempts to date documents in a year level by exploiting relative temporal relations between documents and events , which are very effective for dating documents . Continued advances in algorithms and techniques for this task depend on standardized and reproducible evaluation methodologies for comparing systems . However , timeline summary evaluation is still in its infancy , with competing methodologies currently being explored in international evaluation forums such as TREC . One area of active exploration is how to explicitly represent the units of information that should appear in a " good " summary . In this paper , we study how to incorporate social attention in the generation of timeline summaries . In particular , for a given topic , we capture social attention by learning users ' collective interests in the form of word distributions from Twitter , which are subsequently incorporated into a unified framework for timeline summary generation . We construct four evaluation sets over six diverse topics . The process of counting reads is called read summarization . Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature . RESULTS We present featureCounts , a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments . To tackle this problem , we propose the task of reader-aware abstractive summary generation , which utilizes the reader comments to help the model produce better summary about the main aspect . Unlike traditional abstractive summarization task , reader-aware summarization confronts two main challenges : ( 1 ) Comments are informal and noisy ; ( 2 ) jointly modeling the news document and the reader comments is challenging . To tackle the above challenges , we design an adversarial learning model named reader-aware summary generator ( RASG ) , which consists of four components : ( 1 ) a sequence-to-sequence based summary generator ; ( 2 ) a reader attention module capturing the reader focused aspects ; ( 3 ) a supervisor modeling the semantic gap between the generated summary and reader focused aspects ; ( 4 ) a goal tracker producing the goal for each generation step .
Text summarization is attracting more and more attention while deep neural network has had many successful application in NLP . One problem of such models is its inability to focus on the essentials of documents , thus generating summaries that may not be important , especially during multi-sentence summarization .
The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance . To quickly have an overview of abundant information , Text Summarization ( TS ) comes into play . TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge .
The hybrid model is depending on the linear combination of statistical measures like sentence position , TF-IDF , aggregate similarity , centroid , and sentiment analysis . Our idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message ; hence , it can play vital role in text document summarization . As we know for any sentence , emotions ( calling sentiments ) may be negative , positive , or neutral .
In this paper we describe computational approaches to summarizing dynamically introduced information : online discussions and blogs , and their evaluations . Research in the past has been mainly focused on text-based summarization where the input data is predominantly newswire data . When branching into these newly emerged data types , we face number of difficulties that are discussed here .
It is a tool used to display text data summarization in a visual way very easy to understand . However , it has not been extensively used in teaching , especially in elearning , where it would make a differential advantage .
Sentence ranking is the key issue of text automatic summarization . Based on mutual reinforcement principles , we proposed a new sentence ranking model —— paragraph-sentence mutual reinforcement model .
Among the possible uses it allows , it can be employed for communicating and broadcasting information in real time . The goal of this research is to analyze the task of automatic tweet generation from a text summarization perspective in the context of the journalism genre . To achieve this , different state-of-the-art summarizers are selected and employed for producing multi-lingual tweets in two languages ( English and Spanish ) .
We present a work in progress report of our research on automatically analyzing German court decisions . For accessibility to information retrieval and text summarization processing , we show concepts of a parsing framework dealing with linguistic features of this genre . To cover these features , we first developed a description language inferred from content structure analysis presented before .
Indeed , models that are able to assign a low probability to sentences that are grammatically correct but unlikely may help other tasks in fundamental language understanding like question answering , machine translation , or text summarization . LMs have played a key role in traditional NLP tasks such as speech recognition , machine translation , or text summarization .
paragraph : Multi - source abstractive summarization based RC . The first idea is to use a pointer - generator mechanism for multi - passage RC , which was originally proposed for text summarization SeeLM17 .
section : Datasets We consider three major WMT translation tasks as well as a text summarization task . WMT'16
document : Abstractive Text Summarization using Sequence - to - sequence RNNs and Beyond In this work , we model abstractive text summarization using Attentional Encoder - Decoder Recurrent Neural Networks , and show that they achieve state - of - the - art performance on two different corpora . We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture , such as modeling key - words , capturing the hierarchy of sentence - to - word structure , and emitting words that are rare or unseen at training time .
document : Faithful to the Original : Fact Aware Neural Abstractive Summarization Unlike extractive summarization , abstractive summarization has to fuse different parts of the source text , which inclines to create fake facts . Our preliminary study reveals nearly 30 % of the outputs from a state - of - the - art neural summarization system suffer from this problem .
We experimented in four settings : from English to German ( En - De ) , its reverse ( De - En ) , from English to French ( En - Fr ) , and its reverse ( Fr - En ) . Headline generation is a task that creates a short summarization of an input sentence . rush - chopra - weston:2015:EMNLP constructed a headline generation dataset by extracting pairs of first sentences of news articles and their headlines from the annotated English Gigaword corpus .
We propose a collaborative approach to improve document summarization by incorporating social contextual information into the sentence ranking process . Both the relationships between sentences from document context and the preference information from user context are investigated in the approach .
However , most existing summarizers are general purpose . Thus , they do not consider neither annotations nor user skills during the document summarization process . This paper studies the application of a state-of-the-art summarization system , namely , the itemset-based summarizer ( ItemSum ) , in an e-learning context .
The sentences are picked using simple coverage and orthogonality criteria . We describe a novel combinatorial formulation that captures exactly the document-summarization problem , and we develop simple and efficient algorithms for solving it . We compare our algorithms with many popular document-summarization techniques via a broad set of experiments on real data .
In recent years , algebraic methods , more precisely matrix decomposition approaches , have become a key tool for tackling document summarization problem . Typical algebraic methods used in multi-document summarization ( MDS ) vary from soft and hard clustering approaches to low-rank approximations .
Based on these principles , a greedy yet very efficient entry selection algorithm is proposed . To evaluate the entry selection algorithms , an extrinsic evaluation methodology from document summarization research is adapted . Specifically , we evaluate the proposed entry selection algorithms by examining their blog classification accuracies .
We develop a sentence level selective encoding mechanism to select important feature before extracting sentences , and use a novel reinforcement learning based training algorithm to extend the sequence model . Besides , for single document extractive summarization task , most of researchers only pay attention to the main part of document . We analyze and explore the side information such as the headline and image caption in both CNN and Daily Mail news datasets .
Graph-based models and ranking algorithms have been drawn considerable attentions from the document summarization community in the recent years . However , in regard to query-oriented summarization , the influence of the query has been limited to the sentence nodes in the previous graph models .
To solve the problem of automatic muti-document summarization with spasre topics , the recently proposed fully sparse topic model is introduced into the document summarization field . An automatic summarization algorithm based on topic distribution and words distribution is proposed .
Several examples are illustrated in Table [ reference ] . NLI is in the core of natural language understanding and has wide applications in NLP , e.g. , question answering and automatic summarization . Moreover , NLI is also related to other tasks of sentence pair modeling , including paraphrase detection , relation recognition of discourse units , etc .
Our fast architecture is interesting for character level encoders where the input is significantly longer than for words . Also , we plan to investigate the effectiveness of our architecture on other sequence - tosequence tasks , e.g. summarization , constituency parsing , dialog modeling . section : A Alignment Visualization
They show promising results on their Chinese dataset using an encoder - decoder RNN , but do not report experiments on English corpora . In another very recent work , jianpeng used RNN based encoder - decoder for extractive summarization of documents . This model is not directly comparable to ours since their framework is extractive while ours and that of , and is abstractive .
The vector encodes the ( neighboring ) node and the edge connected to , which can be viewed as a vector representation of the - th candidate action ( excluding the STOP action ) . And the vector is a vector summarization of and , which is used to model the STOP action probability . In summary , we use the sub - vectors to model and according to : where denotes inner product , is a fully - connected neural network with model parameter , denotes the element - wise sigmoid function , and is the softmax function with temperature parameter .
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods . In this paper , we propose the task of abstractive timeline summarization , which tends to concisely paraphrase the information in the time-stamped events .
Automatic headline generation is a sub-task of oneline summarization with many reported applications . Evaluation of systems generating headlines is a very challenging and undeveloped area .
Also , the bursts detection of these items are derived . The co-occurrence of keywords and their bursts detection and timeline review are shown , respectively . Finally , some conclusions are given .
Since many applications such as timeline summaries and temporal IR involving temporal analysis rely on document timestamps , the task of automatic dating of documents has been increasingly important . Instead of using feature-based methods as conventional models , our method attempts to date documents in a year level by exploiting relative temporal relations between documents and events , which are very effective for dating documents .
Continued advances in algorithms and techniques for this task depend on standardized and reproducible evaluation methodologies for comparing systems . However , timeline summary evaluation is still in its infancy , with competing methodologies currently being explored in international evaluation forums such as TREC . One area of active exploration is how to explicitly represent the units of information that should appear in a " good " summary .
In this paper , we study how to incorporate social attention in the generation of timeline summaries . In particular , for a given topic , we capture social attention by learning users ' collective interests in the form of word distributions from Twitter , which are subsequently incorporated into a unified framework for timeline summary generation . We construct four evaluation sets over six diverse topics .
The process of counting reads is called read summarization . Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature . RESULTS We present featureCounts , a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments .
To tackle this problem , we propose the task of reader-aware abstractive summary generation , which utilizes the reader comments to help the model produce better summary about the main aspect . Unlike traditional abstractive summarization task , reader-aware summarization confronts two main challenges : ( 1 ) Comments are informal and noisy ; ( 2 ) jointly modeling the news document and the reader comments is challenging . To tackle the above challenges , we design an adversarial learning model named reader-aware summary generator ( RASG ) , which consists of four components : ( 1 ) a sequence-to-sequence based summary generator ; ( 2 ) a reader attention module capturing the reader focused aspects ; ( 3 ) a supervisor modeling the semantic gap between the generated summary and reader focused aspects ; ( 4 ) a goal tracker producing the goal for each generation step .
Main Point Generator: Summarizing with a Focus Text Summarization: An Essential Study A New Approach for Single Text Document Summarization On the Summarization of Dynamically Introduced Information: Online Discussions and Blogs Word Clouds as a Learning Analytic Tool for the Cooperative e-Learning Platform NeuroK Paragraph-Sentence Mutual Reinforcement Based Automatic Summarization Algorithm Towards automatic tweet generation: A comparative study from the text summarization perspective in the journalism genre A Framework for Graph-Based Parsing of German Private Law Decisions Exploring the Limits of Language Modeling Multi-style Generative Reading Comprehension Convolutional Sequence to Sequence Learning Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond Faithful to the Original: Fact Aware Neural Abstractive Summarization Direct Output Connection for a High-Rank Language Model Improving Document Summarization by Incorporating Social Contextual Information Learning From Summaries: Supporting e-Learning Activities by Means of Document Summarization Highlighting Diverse Concepts in Documents Multi-document summarization via Archetypal Analysis of the content-graph joint model On profiling blogs with representative entries Sentence Selective Neural Extractive Summarization with Reinforcement Learning A Query-Sensitive Graph-Based Sentence Ranking Algorithm for Query-Oriented Multi-document Summarization Multi-document summarization using fully sparse topic models Natural Language Inference by Tree-Based Convolution and Heuristic Matching A Convolutional Encoder Model for Neural Machine Translation Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Learning towards Abstractive Timeline Summarization HEvaS: Headline Evaluation System Studies in Informatics and Control: A Bibliometric Analysis from 2008 to 2019 Event-Based Time Label Propagation for Automatic Dating of News Articles A Comparison of Nuggets and Clusters for Evaluating Timeline Summaries Timeline generation with social attention featureCounts: an efficient general purpose program for assigning sequence reads to genomic features Abstractive Text Summarization by Incorporating Reader Comments
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