A Review on Automatic Text Summarization Approaches

It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

[1]  Naomie Salim,et al.  Fuzzy Genetic Semantic Based Text Summarization , 2011, 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing.

[2]  Dragomir R. Radev,et al.  Experiments in Single and Multi-Document Summarization Using MEAD , 2001 .

[3]  Naomie Salim,et al.  Fuzzy Logic Based Method for Improving Text Summarization , 2009, ArXiv.

[4]  Jack G. Conrad,et al.  Thomson Reuters at TAC 2008: Aggressive Filtering with FastSum for Update and Opinion Summarization , 2008, TAC.

[5]  Naomie Salim,et al.  Opposition Differential Evolution Based Method for Text Summarization , 2013, ACIIDS.

[6]  Xiaojun Wan,et al.  Improved Affinity Graph Based Multi-Document Summarization , 2006, NAACL.

[7]  A. Kogilavani,et al.  Ontology Enhanced Clustering Based Summarization of Medical Documents , 2009 .

[8]  Shanmugasundaram Hariharan,et al.  Studies on Graph Based Approaches for Singleand Multi Document Summarizations , 2009 .

[9]  Ani Nenkova,et al.  Facilitating email thread access by extractive summary generation , 2003, RANLP.

[10]  Khosrow Kaikhah.  Text Summarization Using Neural Networks , 2004 .

[11]  Ani Nenkova,et al.  A Survey of Text Summarization Techniques , 2012, Mining Text Data.

[12]  John Blitzer,et al.  Summarizing archived discussions: a beginning , 2003, IUI '03.

[13]  Michel Galley,et al.  A Skip-Chain Conditional Random Field for Ranking Meeting Utterances by Importance , 2006, EMNLP.

[14]  Naomie Salim,et al.  Swarm Based Text Summarization , 2009, 2009 International Association of Computer Science and Information Technology - Spring Conference.

[15]  Yonggang Zhang,et al.  Co-clustering Sentences and Terms for Multi-document Summarization , 2011, CICLing.

[16]  Furu Wei,et al.  A document-sensitive graph model for multi-document summarization , 2010, Knowledge and Information Systems.

[17]  Dragomir R. Radev,et al.  Generating Natural Language Summaries from Multiple On-Line Sources , 1998, CL.

[18]  Brigitte Endres-Niggemeyer,et al.  Scenario Forms for Web Information Seeking and Summarizing in Bone Marrow Transplantation , 2002, COLING 2002.

[19]  Karen Spärck Jones.  Automatic summarising: factors and directions , 1998, ArXiv.

[20]  David Evans,et al.  Tracking and summarizing news on a daily basis with Columbia's Newsblaster , 2002 .

[21]  Dou Shen.  Text Summarization , 2009, Encyclopedia of Database Systems.

[22]  Kathleen R. McKeown,et al.  SIMFINDER: A Flexible Clustering Tool for Summarization , 2001 .

[23]  Zhu Zhang,et al.  Towards CST-enhanced summarization , 2002, AAAI/IAAI.

[24]  Naomie Salim,et al.  Differential evolution cluster-based text summarization methods , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

[25]  Youngjoong Ko,et al.  An effective sentence-extraction technique using contextual information and statistical approaches for text summarization , 2008, Pattern Recognition Letters.

[26]  Thierry Poibeau,et al.  Automatic Text Summarization: Past, Present and Future , 2013, Multi-source, Multilingual Information Extraction and Summarization.

[27]  Eduard H. Hovy,et al.  Automated Text Summarization and the SUMMARIST System , 1998, TIPSTER.

[28]  Thiago A. S. Pardo,et al.  Experiments with CST-Based Multidocument Summarization , 2010, TextGraphs@ACL.

[29]  Francine Chen,et al.  A trainable document summarizer , 1995, SIGIR '95.

[30]  Liang Zhou,et al.  On the Summarization of Dynamically Introduced Information: Online Discussions and Blogs , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[31]  Dragomir R. Radev,et al.  Centroid-based summarization of multiple documents , 2004, Inf. Process. Manag..

[32]  Daniel Marcu,et al.  The automatic construction of large-scale corpora for summarization research , 1999, SIGIR '99.

[33]  Naomie Salim,et al.  A CLUSTERED SEMANTIC GRAPH APPROACH FOR MULTI-DOCUMENT ABSTRACTIVE SUMMARIZATION , 2015 .

[34]  Chao-Lin Liu,et al.  Ontology-based Text Summarization for Business News Articles , 2003, CATA.

[35]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[36]  Lehana Thabane,et al.  Assessment of the quality and variability of health information on chronic pain websites using the DISCERN instrument , 2010, BMC Medicine.

[37]  Ee-Peng Lim,et al.  Comments-oriented blog summarization by sentence extraction , 2007, CIKM '07.

[38]  Xiaojun Wan,et al.  An Exploration of Document Impact on Graph-Based Multi-Document Summarization , 2008, EMNLP.

[39]  Hans Peter Luhn,et al.  The Automatic Creation of Literature Abstracts , 1958, IBM J. Res. Dev..

[40]  Ani Nenkova,et al.  A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization , 2006, SIGIR.

[41]  Ramiz M. Aliguliyev,et al.  A new sentence similarity measure and sentence based extractive technique for automatic text summarization , 2009, Expert Syst. Appl..

[42]  Ramiz M. Aliguliyev,et al.  CLUSTERING TECHNIQUES AND DISCRETE PARTICLE SWARM OPTIMIZATION ALGORITHM FOR MULTI‐DOCUMENT SUMMARIZATION , 2010, Comput. Intell..

[43]  Naomie Salim,et al.  Multi document summarization based on cross-document relation using voting technique , 2013, 2013 INTERNATIONAL CONFERENCE ON COMPUTING, ELECTRICAL AND ELECTRONIC ENGINEERING (ICCEEE).

[44]  Mark T. Maybury,et al.  Automatic Summarization , 2002, Computational Linguistics.

[45]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[46]  Dragomir R. Radev,et al.  LexRank: Graph-based Lexical Centrality as Salience in Text Summarization , 2004, J. Artif. Intell. Res..

[47]  Lucy Vanderwende,et al.  Enhancing Single-Document Summarization by Combining RankNet and Third-Party Sources , 2007, EMNLP.

[48]  Min-Yen Kan,et al.  Applying Natural Language Generation to Indicative Summarization , 2001, EWNLG@ACL.

[49]  Saswati Mukherjee,et al.  A classification-based summarisation model for summarising text documents , 2014, Int. J. Inf. Commun. Technol..

[50]  Eleazar Eskin,et al.  Detecting Text Similarity over Short Passages: Exploring Linguistic Feature Combinations via Machine Learning , 1999, EMNLP.

[51]  Owen Rambow,et al.  Summarizing Email Threads , 2004, NAACL.

[52]  Claire Cardie,et al.  Multidocument Summarization via Information Extraction , 2001, HLT.

[53]  Regina Barzilay,et al.  Towards Multidocument Summarization by Reformulation: Progress and Prospects , 1999, AAAI/IAAI.

[54]  Tao Li,et al.  Ontology-enriched multi-document summarization in disaster management , 2010, SIGIR.

[55]  H. P. Edmundson,et al.  New Methods in Automatic Extracting , 1969, JACM.

[56]  Pascale Fung,et al.  One story, one flow: Hidden Markov Story Models for multilingual multidocument summarization , 2006, TSLP.

[57]  Rada Mihalcea,et al.  TextRank: Bringing Order into Text , 2004, EMNLP.

[58]  Halil Kilicoglu,et al.  Automatic summarization of MEDLINE citations for evidence-based medical treatment: A topic-oriented evaluation , 2009, J. Biomed. Informatics.

[59]  Sergey Brin,et al.  Reprint of: The anatomy of a large-scale hypertextual web search engine , 2012, Comput. Networks.

[60]  René Witte,et al.  Ontology-Based Extraction and Summarization of Protein Mutation Impact Information , 2010, BioNLP@ACL.

[61]  Vasileios Hatzivassiloglou,et al.  A Formal Model for Information Selection in Multi-Sentence Text Extraction , 2004, COLING.

[62]  Gurpreet Singh Lehal,et al.  A Survey of Text Summarization Extractive Techniques , 2010 .

[63]  Min-Yen Kan,et al.  Customization in a unified framework for summarizing medical literature , 2005, Artif. Intell. Medicine.

[64]  Fumiyo Fukumoto,et al.  Multi-document Summarization Using Link Analysis Based on Rhetorical Relations between Sentences , 2011, CICLing.

[65]  Regina Barzilay,et al.  Information Fusion in the Context of Multi-Document Summarization , 1999, ACL.

[66]  Zheng-Yu Niu,et al.  Multi-document Summarization Using a Clustering-Based Hybrid Strategy , 2006, AIRS.

[67]  Dragomir R. Radev,et al.  Sub-event based multi-document summarization , 2003, HLT-NAACL 2003.

[68]  James H. Martin,et al.  Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .

[69]  Rakesh M. Verma,et al.  A Semantic Free-text Summarization System Using Ontology Knowledge , 2007 .

[70]  Naomie Salim,et al.  Multi document summarization based on news components using fuzzy cross-document relations , 2014, Appl. Soft Comput..

[71]  Frank Schilder,et al.  FastSum: Fast and Accurate Query-based Multi-document Summarization , 2008, ACL.

[72]  Ani Nenkova,et al.  The Impact of Frequency on Summarization , 2005 .

[73]  Alex Alves Freitas,et al.  Automatic Text Summarization Using a Machine Learning Approach , 2002, SBIA.

[74]  Rose Dieng,et al.  An Ontology-based Approach to Support Text Mining and Information Retrieval in the Biological Domain , 2007, J. Univers. Comput. Sci..

[75]  Zerina Begum,et al.  Literature Review of Automatic Multiple Documents Text Summarization , 2013 .

[76]  Dragomir R. Radev.  A Common Theory of Information Fusion from Multiple Text Sources Step One: Cross-Document Structure , 2000, SIGDIAL Workshop.

[77]  Jiawei Han,et al.  Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions , 2010, COLING.

[78]  Chang-Shing Lee,et al.  A fuzzy ontology and its application to news summarization , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[79]  S. A. Babar,et al.  Improving Performance of Text Summarization , 2015 .

[80]  Dragomir R. Radev,et al.  Generating summaries of multiple news articles , 1995, SIGIR '95.

[81]  Jack G. Conrad,et al.  Query-based opinion summarization for legal blog entries , 2009, ICAIL.

[82]  Jack G. Conrad,et al.  Polarity Filtering for Sentiment Summarization , 2008 .

[83]  Beth Sundheim,et al.  Overview of the Fourth Message Understanding Evaluation and Conference , 1992, MUC.