DOCSLIB.ORG

Automatic Text Summarization: a Review

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Automatic Text Summarization: a Review eKNOW 2017 : The Ninth International Conference on Information, Process, and Knowledge Management Automatic Text Summarization: A review Naima Zerari, Samia Aitouche, Mohamed Djamel Mouss, Asma Yaha Automation and Manufacturing Laboratory Department of Industrial Engineering Batna 2 University Batna, Algeria Email: [email protected], [email protected], [email protected], [email protected] Abstract—As we move into the 21st century, with very rapid that the user wants to get [4]. With document summary mobile communication and access to vast stores of information, available, users can easily decide its relevancy to their we seem to be surrounded by more and more information, with interests and acquire desired documents with much less less and less time or ability to digest it. The creation of the mental loads involved. [5]. automatic summarization was really a genius human solution to solve this complicated problem. However, the application of Furthermore, the goal of automatic text summarization is this solution was too complex. In reality, there are many to condense the documents into a shorter version and problems that need to be addressed before the promises of preserve important contents [3]. Text Summarization automatic text summarization can be fully realized. Basically, methods can be classified into two major methods extractive it is necessary to understand how humans summarize the text and abstractive summarization [6]. and then build the system based on that. Yet, individuals are so The rest of the paper is organized as follows: Section 2 is different in their thinking and interpretation that it is hard to about text summarization, precisely the definition of the create "gold-standard" summary against which output summaries will be evaluated. In this paper, we will discuss the summary; Section 3 describes the automatic text basic concepts of this topic by giving the most relevant summarization; Section 4 depicts the models of automatic definitions, characterizations, types and the two different text summarization; Section 5 defines the summaries approaches of automatic text summarization: extraction and characteristics; Section 6 presents a brief review of the two abstraction. Special attention is devoted to the extractive text summarization methods and finally Section 7 concludes approach. It consists of selecting important sentences and this paper and outlines the envisaged research work. paragraphs from the original text and concatenating them into shorter form. Broadly, the importance of sentences is decided based on statistical features of sentences. This approach avoids II. TEXT SUMMARIZATION any efforts on deep text understanding. It is conceptually simple and easy to implement. The human being needs a summary mainly because it reduces reading time and it makes the selection process Keywords- Text summarization; Automatic text easier during the search of document process. summarization; Abstractive approach; Extractive approach; Text summarization can be used by various applications; Natural language processing. for instance researchers need a summary for deciding whether to read the entire document or not and for I. INTRODUCTION summarizing information searched by user on Internet. Summarizing documents involves cognitive effort from the The rapid evolution of WWW has made huge quantity of summarizer: different fragments of a text must be selected, documents on a variety of topics available to the users reformulated and assembled according to their relevance. [1][2]. To exploit these documents effectively, it is required The coherence of the information included in the summary to be able to get a summary of them. However, it is very must also be taken into account [7]. Thus, text difficult for humans to create a hand written summary of the summarization, the reduction of a text to its essential content, entire available document. Automatic Text Summarization is a task that requires linguistic competence, world (ATS) provides a solution to this information overload knowledge, and intelligence [7]. The subfield of problem [2]. Hence, ATS has become an important and summarization has been investigated by the Natural timely tool for user to quickly understand the large volume Language Processing (NLP) community for nearly the last of information [3]. The automatic summarization included half century. Radev et al [8] define a summary as: “a text that in language processing field, is the process of dealing with a is produced from one or more texts that convey important large amount of information by comprising only the essential information in the original text, and that is no longer than ones. It often occurs in everyday communication and it is an half of the original text(s) and usually significantly less than important and professional skill for some people. Automatic that”. This simple definition captures three important aspects text summarization aims at providing a condensed that characterize research on automatic summarization [8]: representation of the content according to the information Copyright (c) IARIA, 2017. ISBN: 978-1-61208-542-5 20 eKNOW 2017 : The Ninth International Conference on Information, Process, and Knowledge Management . Summaries may be produced from a single document most of the abstractive summarization methods produce or multiple documents. highly coherent, cohesive, rich information and less . Summaries should preserve important information. redundant summary. Munot and Govilkar [12] discussed in details the two main categories of text summarization . Summaries should be short. methods and also presented a taxonomy of summarization The summary done by means of a computer, i.e., systems, statistical and linguistic approaches for automatically, is called Automatic Text Summarization. summarization. Sariki et al [2] proposed a system to generate a summary of a single document, specifying the III. AUTOMATIC TEXT SUMMARIZATION keywords and adjusting the length of the final summary to Automatic text summarization is the technique which produce. The proposed system has been improved a lot in compresses a large text to a shorter text which includes the accuracy. The authors precise also that the generated important information. The computer program is given a text summary can be visualized in the form of a Power Point and it returns a summary of the original text. This is done by presentation (PPT), thus making it easy for the user to create reducing redundancy of the text and by extracting the an effective classroom presentation. So, they propose to essence of the text [9]. Generally, a summary should be extend their work to multiple documents in future. Chandra much shorter than the source text. This characteristic is et al [5] proposed K-mixture semantic relationship defined by the compression rate, which measures the ratio of significance (KSRS) approach. It is a statistical approach to length of summary to the length of original text [3]. The first text summarization. The proposed approach combines the effort on automatic text summarization system was made in K- mixture term weighting scheme, based on a the late 1950. This automatic summarizer selects significant mathematical (probabilistic) ground, and the linguistic sentences from the document and concatenates them together technique. This latter explores term relationships by finding [3]. Currently automatic text summarization has benefited the semantic relationship significance of nouns that signifies from the expertise of a range of fields of research: term and sentence semantics. The authors specified that the information retrieval and information extraction, natural proposed approach, KSRS, performs better and language generation, discourse studies, machine learning and consequently its feasibility in text summarization technical studies used by professional summarizers [7]. applications is justifiable. Also, they specified that its use Summaries can be divided in two main categories: extractive allows the choice of a lower summary proportion without and abstractive. worrying about the performance deterioration. An abstractive summarization tries to develop an IV. AUTOMATIC TEXT SUMMARIZATION MODELS understanding of the main concepts in a document and then express those concepts in clear natural language. It uses Depending upon the number of documents accepted as linguistic methods to study the text and then to find the new input by a summarization process, automatic text concepts and expressions to best describe it by generating a summarization can be categorized as single document new shorter text that conveys the salient information from summarization and multi-document summarization as shown the original text document [6]. This method is the more in Fig. 1 below. difficult and it is poorly practical. It is highly complex as it In the model Single Document Text Summarization, a needs extensive natural language processing. summary is produced from single input document. The An extractive summarization consists of selecting important single document summarization process flow can be sentences or paragraphs from the original document and depicted in Fig. 2. However, in Multi Document Text concatenating them into shorter form. The importance of Summarization, a summary is produced from multiple input sentences is decided based on statistical and linguistic documents dealing with the same topic as illustrate in Fig. 3. features of sentences [6]. This method is fairly applicable In 1995, Radev and McKeown [13] were the first
Load more

Recommended publications
  • Automatic Summarization of Student Course Feedback
    Automatic Summarization of Student Course Feedback Wencan Luo† Fei Liu‡ Zitao Liu† Diane Litman† †University of Pittsburgh, Pittsburgh, PA 15260 ‡University of Central Florida, Orlando, FL 32716 wencan, ztliu, litman @cs.pitt.edu [email protected] { } Abstract Prompt Describe what you found most interesting in today’s class Student course feedback is generated daily in Student Responses both classrooms and online course discussion S1: The main topics of this course seem interesting and forums. Traditionally, instructors manually correspond with my major (Chemical engineering) analyze these responses in a costly manner. In S2: I found the group activity most interesting this work, we propose a new approach to sum- S3: Process that make materials marizing student course feedback based on S4: I found the properties of bike elements to be most the integer linear programming (ILP) frame- interesting work. Our approach allows different student S5: How materials are manufactured S6: Finding out what we will learn in this class was responses to share co-occurrence statistics and interesting to me alleviates sparsity issues. Experimental results S7: The activity with the bicycle parts on a student feedback corpus show that our S8: “part of a bike” activity approach outperforms a range of baselines in ... (rest omitted, 53 responses in total.) terms of both ROUGE scores and human eval- uation. Reference Summary - group activity of analyzing bicycle’s parts - materials processing - the main topic of this course 1 Introduction Table 1: Example student responses and a reference summary Instructors love to solicit feedback from students. created by the teaching assistant. ‘S1’–‘S8’ are student IDs.
    [Show full text]
  • Using N-Grams to Understand the Nature of Summaries
    Using N-Grams to Understand the Nature of Summaries Michele Banko and Lucy Vanderwende One Microsoft Way Redmond, WA 98052 {mbanko, lucyv}@microsoft.com views of the event being described over different Abstract documents, or present a high-level view of an event that is not explicitly reflected in any single document. A Although single-document summarization is a useful multi-document summary may also indicate the well-studied task, the nature of multi- presence of new or distinct information contained within document summarization is only beginning to a set of documents describing the same topic (McKeown be studied in detail. While close attention has et. al., 1999, Mani and Bloedorn, 1999). To meet these been paid to what technologies are necessary expectations, a multi-document summary is required to when moving from single to multi-document generalize, condense and merge information coming summarization, the properties of human- from multiple sources. written multi-document summaries have not Although single-document summarization is a well- been quantified. In this paper, we empirically studied task (see Mani and Maybury, 1999 for an characterize human-written summaries overview), multi-document summarization is only provided in a widely used summarization recently being studied closely (Marcu & Gerber 2001). corpus by attempting to answer the questions: While close attention has been paid to multi-document Can multi-document summaries that are summarization technologies (Barzilay et al. 2002, written by humans be characterized as Goldstein et al 2000), the inherent properties of human- extractive or generative? Are multi-document written multi-document summaries have not yet been summaries less extractive than single- quantified.
    [Show full text]
  • Automatic Summarization of Medical Conversations, a Review Jessica Lopez
    Automatic summarization of medical conversations, a review Jessica Lopez To cite this version: Jessica Lopez. Automatic summarization of medical conversations, a review. TALN-RECITAL 2019- PFIA 2019, Jul 2019, Toulouse, France. pp.487-498. hal-02611210 HAL Id: hal-02611210 https://hal.archives-ouvertes.fr/hal-02611210 Submitted on 30 May 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Jessica López Espejel Automatic summarization of medical conversations, a review Jessica López Espejel 1, 2 (1) CEA, LIST, DIASI, F-91191 Gif-sur-Yvette, France. (2) Paris 13 University, LIPN, 93430 Villateneuse, France. [email protected] RÉSUMÉ L’analyse de la conversation joue un rôle important dans le développement d’appareils de simulation pour la formation des professionnels de la santé (médecins, infirmières). Notre objectif est de développer une méthode de synthèse automatique originale pour les conversations médicales entre un patient et un professionnel de la santé, basée sur les avancées récentes en matière de synthèse à l’aide de réseaux de neurones convolutionnels et récurrents. La méthode proposée doit être adaptée aux problèmes spécifiques liés à la synthèse des dialogues. Cet article présente une revue des différentes méthodes pour les résumés par extraction et par abstraction et pour l’analyse du dialogue.
    [Show full text]
  • Exploring Sentence Vector Spaces Through Automatic Summarization
    Under review as a conference paper at ICLR 2018 EXPLORING SENTENCE VECTOR SPACES THROUGH AUTOMATIC SUMMARIZATION Anonymous authors Paper under double-blind review ABSTRACT Vector semantics, especially sentence vectors, have recently been used success- fully in many areas of natural language processing. However, relatively little work has explored the internal structure and properties of spaces of sentence vectors. In this paper, we will explore the properties of sentence vectors by studying a par- ticular real-world application: Automatic Summarization. In particular, we show that cosine similarity between sentence vectors and document vectors is strongly correlated with sentence importance and that vector semantics can identify and correct gaps between the sentences chosen so far and the document. In addition, we identify specific dimensions which are linked to effective summaries. To our knowledge, this is the first time specific dimensions of sentence embeddings have been connected to sentence properties. We also compare the features of differ- ent methods of sentence embeddings. Many of these insights have applications in uses of sentence embeddings far beyond summarization. 1 INTRODUCTION Vector semantics have been growing in popularity for many other natural language processing appli- cations. Vector semantics attempt to represent words as vectors in a high-dimensional space, where vectors which are close to each other have similar meanings. Various models of vector semantics have been proposed, such as LSA (Landauer & Dumais, 1997), word2vec (Mikolov et al., 2013), and GLOVE(Pennington et al., 2014), and these models have proved to be successful in other natural language processing applications. While these models work well for individual words, producing equivalent vectors for sentences or documents has proven to be more difficult.
    [Show full text]
  • Keyphrase Based Evaluation of Automatic Text Summarization
    International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 7, May 2015 Keyphrase based Evaluation of Automatic Text Summarization Fatma Elghannam Tarek El-Shishtawy Electronics Research Institute Faculty of Computers and Information Cairo, Egypt Benha University, Benha, Egypt ABSTRACT KpEval idea is to count the matches between the peer The development of methods to deal with the informative summary and reference summaries for the essential parts of contents of the text units in the matching process is a major the summary text. KpEval have three main modules, i) challenge in automatic summary evaluation systems that use lemma extractor module that breaks the text into words and fixed n-gram matching. The limitation causes inaccurate extracts their lemma forms and the associated lexical and matching between units in a peer and reference summaries. syntactic features, ii) keyphrase extractor that extracts The present study introduces a new Keyphrase based important keyphrases in their lemma forms, and iii) the Summary Evaluator (KpEval) for evaluating automatic evaluator that scoring the summary based on counting the summaries. The KpEval relies on the keyphrases since they matched keyphrases occur between the peer summary and one convey the most important concepts of a text. In the or more reference summaries. The remaining of this paper is evaluation process, the keyphrases are used in their lemma organized as follows: Section 2 reviews the previous works; form as the matching text unit. The system was applied to Section 3 the proposed keyphrase based summary evaluator; evaluate different summaries of Arabic multi-document data Section 4 discusses the performance evaluation; and section 5 set presented at TAC2011.
    [Show full text]
  • Latent Semantic Analysis and the Construction of Coherent Extracts
    Latent Semantic Analysis and the Construction of Coherent Extracts Tristan Miller German Research Center for Artificial Intelligence0 Erwin-Schrodinger-Straße¨ 57, D-67663 Kaiserslautern [email protected] Keywords: automatic summarization, latent semantic analy- many of these techniques are tied to a particular sis, LSA, coherence, extracts language or require resources such as a list of dis- Abstract course keywords and a manually marked-up cor- pus; others are constrained in the type of summary We describe a language-neutral au- they can generate (e.g., general-purpose vs. query- tomatic summarization system which focussed). aims to produce coherent extracts. It In this paper, we present a new, recursive builds an initial extract composed solely method for automatic text summarization which of topic sentences, and then recursively aims to preserve both the topic coverage and fills in the topical lacunae by provid- the coherence of the source document, yet has ing linking material between semanti- minimal reliance on language-specific NLP tools. cally dissimilar sentences. While exper- Only word- and sentence-boundary detection rou- iments with human judges did not prove tines are required. The system produces general- a statistically significant increase in tex- purpose extracts of single documents, though it tual coherence with the use of a latent should not be difficult to adapt the technique semantic analysis module, we found a to query-focussed summarization, and may also strong positive correlation between co- be of use in improving the coherence of multi- herence and overall summary quality. document summaries. 2 Latent semantic analysis 1 Introduction Our system fits within the general category of IR- A major problem with automatically-produced based systems, but rather than comparing text with summaries in general, and extracts in particular, the standard vector-space model, we employ la- is that the output text often lacks fluency and orga- tent semantic analysis (LSA) [Deerwester et al., nization.
    [Show full text]
  • Leveraging Word Embeddings for Spoken Document Summarization
    Leveraging Word Embeddings for Spoken Document Summarization Kuan-Yu Chen*†, Shih-Hung Liu*, Hsin-Min Wang*, Berlin Chen#, Hsin-Hsi Chen† *Institute of Information Science, Academia Sinica, Taiwan #National Taiwan Normal University, Taiwan †National Taiwan University, Taiwan * # † {kychen, journey, whm}@iis.sinica.edu.tw, [email protected], [email protected] Abstract without human annotations involved. Popular methods include Owing to the rapidly growing multimedia content available on the vector space model (VSM) [9], the latent semantic analysis the Internet, extractive spoken document summarization, with (LSA) method [9], the Markov random walk (MRW) method the purpose of automatically selecting a set of representative [10], the maximum marginal relevance (MMR) method [11], sentences from a spoken document to concisely express the the sentence significant score method [12], the unigram most important theme of the document, has been an active area language model-based (ULM) method [4], the LexRank of research and experimentation. On the other hand, word method [13], the submodularity-based method [14], and the embedding has emerged as a newly favorite research subject integer linear programming (ILP) method [15]. Statistical because of its excellent performance in many natural language features may include the term (word) frequency, linguistic processing (NLP)-related tasks. However, as far as we are score, recognition confidence measure, and prosodic aware, there are relatively few studies investigating its use in information. In contrast, supervised sentence classification extractive text or speech summarization. A common thread of methods, such as the Gaussian mixture model (GMM) [9], the leveraging word embeddings in the summarization process is Bayesian classifier (BC) [16], the support vector machine to represent the document (or sentence) by averaging the word (SVM) [17], and the conditional random fields (CRFs) [18], embeddings of the words occurring in the document (or usually formulate sentence selection as a binary classification sentence).
    [Show full text]
  • Automatic Summarization and Readability
    COGNITIVE SCIENCE MASTER THESIS Automatic summarization and Readability LIU-IDA/KOGVET-A–11/004–SE Author: Supervisor: Christian SMITH Arne JONSSON¨ [email protected] [email protected] List of Figures 2.1 A simplified graph where sentences are linked and weighted ac- cording to the cosine values between them. 10 3.1 Evaluation of summaries on different dimensionalities. The X-axis denotes different dimensionalities, the Y-axis plots the mean value from evaluations on several seeds on each dimensionality. 18 3.2 The iterations of PageRank. The figure depicts the ranks of the sentences plotted on the Y-axis and the iterations on the X-axis. Each series represents a sentence. 19 3.3 The iterations of PageRank in different dimensionalities of the RI- space. The figure depicts 4 different graphs, each representing the trial of a specific setting of the dimensionality. From the left the dimensionalities of 10, 100, 300 and 1000 were used. The ranks of the sentences is plotted on the Y-axis and the iterations on the X-axis. Each series represents a sentence. 20 3.4 Figures of sentence ranks on different damping factors in PageRank 21 3.5 Effect of randomness, same text and settings on ten different ran- dom seeds. The final ranks of the sentences is plotted on the Y, with the different seeds on X. The graph depicts 9 trials at follow- ing dimensionalities, from left: 10, 20, 50, 100, 300, 500, 1000, 2000, 10000. 22 3.6 Values sometimes don’t converge on smaller texts. The left graph depicts the text in d=100 and the right graph d=20.
    [Show full text]
  • Question-Based Text Summarization
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Drexel Libraries E-Repository and Archives Question-based Text Summarization A Thesis Submitted to the Faculty of Drexel University by Mengwen Liu in partial fulfillment of the requirements for the degree of Doctor of Philosophy December 2017 c Copyright 2017 Mengwen Liu. All Rights Reserved. ii Dedications This thesis is dedicated to my family iii Acknowledgments I would like to thank to my advisor, Prof. Xiaohua Tony Hu for his generous guidance and support in the past five and a half years. Prof. Hu provided a flexible environment for me to explore different research fields. He encouraged me to work with intellectual peers and attend professional activities, and gave immediate suggestions on my studies and career development. I am also grateful to Prof. Yuan An, whom I worked closely in the first two years of my doctoral study. I thank to him for offering guidance about research, paper review, collaboration with professionals from other fields, and scientific writings. I would like to thank my dissertation committee of Prof. Weimao Ke, Prof. Erjia Yan, Prof. Bei Yu, and Prof. Yi Fang. Prof. Ke provided many suggestions towards my research projects that relate to information retrieval and my thesis topic. Prof. Yan offered me lots of valuable suggestions for my prospectus and thesis and encouraged me to conduct long-term research. Prof. Yu inspired my interest in natural language processing and text mining, and taught me how to do research from scratch with great patience.
    [Show full text]
  • Automatic Text Summarization: Past, Present and Future Horacio Saggion, Thierry Poibeau
    Automatic Text Summarization: Past, Present and Future Horacio Saggion, Thierry Poibeau To cite this version: Horacio Saggion, Thierry Poibeau. Automatic Text Summarization: Past, Present and Future. T. Poibeau; H. Saggion. J. Piskorski, R. Yangarber. Multi-source, Multilingual Information Extraction and Summarization, Springer, pp.3-13, 2012, Theory and Applications of Natural Language Process- ing, 978-3-642-28569-1. hal-00782442 HAL Id: hal-00782442 https://hal.archives-ouvertes.fr/hal-00782442 Submitted on 27 Jul 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Chapter 1 Automatic Text Summarization: Past, Present and Future Horacio Saggion and Thierry Poibeau Abstract Automatic text summarization, the computer-based production of con- densed versions of documents, is an important technology for the information soci- ety. Without summaries it would be practically impossible for human beings to get access to the ever growing mass of information available online. Although research in text summarization is over fifty years old, some efforts are still needed given the insufficient quality of automatic summaries and the number of interesting summa- rization topics being proposed in different contexts by end users (“domain-specific summaries”, “opinion-oriented summaries”, “update summaries”, etc.).
    [Show full text]
  • An Automatic Text Summarization: a Systematic Review
    EasyChair Preprint № 869 An Automatic Text Summarization: A Systematic Review Vishwa Patel and Nasseh Tabrizi EasyChair preprints are intended for rapid dissemination of research results and are integrated with the rest of EasyChair. March 31, 2019 An Automatic Text Summarization: A Systematic Review Vishwa Patel1;2 and Nasseh Tabrizi1;3 1 East Carolina University, Greenville NC 27858, USA 2 [email protected] 3 [email protected] Abstract. The 21st century has become a century of information over- load, where in fact information related to even one topic (due to its huge volume) takes a lot of time to manually summarize it into few lines. Thus, in order to address this problem, Automatic Text Summarization meth- ods have been developed. Generally, there are two approaches that are currently being used in practice: Extractive and Abstractive methods. In this paper, we have reviewed papers from IEEE and ACM libraries those related to Automatic Text Summarization for the English language. Keywords: Automatic Text Summarization · Extractive · Abstractive · Review paper 1 Introduction Nowadays, the Internet has become one of the important sources of information. We surf the Internet, to find some information related to a certain topic. But search engines often return an excessive amount of information for the end user to read increasing the need for Automatic Text Summarization(ATS), increased in this modern era. The ATS will not only save time but also provide important insight into a piece of information. Many years ago scientists started working on ATS but the peak of interest in it started from the year 2000. In the early 21st century, new technologies emerged in the field of Natural Language Processing (NLP) to enhance the capabilities of ATS.
    [Show full text]
  • The Role of Semantics in Automatic Summarization: a Feasibility Study
    The Role of Semantics in Automatic Summarization: A Feasibility Study Tal Baumel Michael Elhadad Computer Science Department Computer Science Department Ben-Gurion University Ben-Gurion University Beer-Sheva, Israel Beer-Sheva, Israel [email protected] [email protected] appropriate pronoun resolution would match Abstract the pronouns to the correct entities; High lexical coverage does not mean co- State-of-the-art methods in automatic summarization herent summaries: the focus on current rely almost exclusively on extracting salient sentences summarization research has been to optimize from input texts. Such extractive methods succeed in ROUGE results. ROUGE measures lexical producing summaries which capture salient infor- overlap between manual summaries and the mation but fail to produce fluent and coherent sum- maries. Recent progress in robust semantic analysis candidate one. We observe, however, that makes the application of semantic techniques to summarizers that achieve the highest summarization relevant. We review in this paper areas ROUGE scores often obtain low manual in the field of summarization that can benefit from the scores because they produce non-cohesive introduction of the type of semantic analysis that has and sometimes non-coherent summaries. become available. The main pain points that semantic information can alleviate are: aiming for more fluent We address those limitations of existing methods summaries by exploiting logical form representation based exclusively on lexical analysis by using of the source text; identifying salient information and intermediate semantic annotations for both sum- avoiding redundancy by relying on textual entailment marization generation and evaluation. and paraphrase identification; and generating a coher- ent summary while relying on rhetorical structure and discourse structure information extracted from the Summarization Generation source documents.
    [Show full text]