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Comparative Study of Text Summarization Methods

by Nikita Munot, Sharvari S. Govilkar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 12
Year of Publication: 2014
Authors: Nikita Munot, Sharvari S. Govilkar
10.5120/17870-8810

Nikita Munot, Sharvari S. Govilkar . Comparative Study of Text Summarization Methods. International Journal of Computer Applications. 102, 12 ( September 2014), 33-37. DOI=10.5120/17870-8810

@article{ 10.5120/17870-8810,
author = { Nikita Munot, Sharvari S. Govilkar },
title = { Comparative Study of Text Summarization Methods },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 12 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number12/17870-8810/ },
doi = { 10.5120/17870-8810 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:33:22.226740+05:30
%A Nikita Munot
%A Sharvari S. Govilkar
%T Comparative Study of Text Summarization Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 12
%P 33-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Text summarization is one of application of natural language processing and is becoming more popular for information condensation. Text summarization is a process of reducing the size of original document and producing a summary by retaining important information of original document. This paper gives comparative study of various text summarization methods based on different types of application. The paper discusses in detail two main categories of text summarization methods these are extractive and abstractive summarization methods. The paper also presents taxonomy of summarization systems and statistical and linguistic approaches for summarization.

References
  1. Saeedeh Gholamrezazadeh, Mohsen Amini Salehi, Bahareh Gholamzadeh. A Comprehensive Survey on Text Summarization Systems. 2009 In proceeding of: Computer Science and its Applications, 2nd International Conference.
  2. Goldstein, J. , Kantrowitz, M. , Mittal, V. , Carbonell, J. , 1999. Summarizing text documents: Sentence selection and evaluation metrics. In: Proc. ACM-SIGIR'99, pp. 121–128.
  3. Horacio, L. Guy. Generating indicative-informative summaries with SumUM : Summarization. Computational linguistics -Association for Computational Linguistics, 2002, vol. 28, pp. 497-526.
  4. Luhn, H. P. , 1959. The automatic creation of literature abstracts. IBM J. Res. Develop. , 159–165.
  5. ZHANG Pei-ying, LI Cun-he. Automatic text summarization based on sentences clustering and extraction.
  6. Barzilay, R. , Elhadad, M. Using Lexical Chains for Text Summarization. In Proc. ACL/EACL'97 Workshop on Intelligent Scalable Text summarization, Madrid, Spain,1997, pp. 10–17.
  7. Youngjoong Koa, Jungyun Seo 2008. An effective sentence-extraction technique using contextual information and statistical approaches for text summarization.
  8. Eduard Hovy and Chin Yew Lin. Automated text summarization in SUMMARIST. MIT Press, 1999, pages 81–94.
  9. Morris, J. , and Hirst, G. 1991. Lexical cohesion computed by thesaural relations as an indicator of the structure of text. Computational Linguistics 17(1):21–43.
  10. Silber G. H. , Kathleen F. McCoy. Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization. Computational Linguistics 28(4): 487-496, 2002.
  11. Kedar Bellare, Anish Das Sharma, Atish Das Sharma, Navneet Loiwal and Pushpak Bhattacharyya. Generic Text Summarization Using Word net. Language Resources Engineering Conference (LREC 2004), Barcelona, May, 2004.
  12. J. Leskovec, M. Grobelnik, N. Milic-Frayling. Extracting Summary Sentences Based on the Document Semantic Graph. Microsoft Research, 2005.
  13. D. Radev, E. Hovy, K. McKeown, "Introduction to the Special Issue on Summarization", Computational Linguistics, Vol. 28, No. 4, pp. 399-408, 2002.
  14. Eduard Hovy and Chin Yew Lin, "Automated text summarization in SUMMARIST", MIT Press, 1999, pages 81–94.
  15. Http://Web. science. mq. edu. au/swan/summarization/projects_full. html.
  16. Edmundson, H. P. , 1968. New methods in automatic extraction. J. ACM16 (2), 264–285. S.
  17. Kupiec, Julian M, Schuetze, Hinrich, "System for genre-specific summarization of documents", Xerox Corporation, 2004.
  18. Martin Hassel, Nima Mazdak, "A Persian text summarizer", International Conference on Computational Linguistics, 2004.
  19. William P. Doran, Nicola Stokes, John Dunnion, and Joe Carthy, "Comparing lexical chain-based summarization approaches using an extrinsic evaluation," In Proc. Global Word net Conference (GWC 2004), 2004.
Index Terms

Computer Science
Information Sciences

Keywords

NLP text summarization abstractive summary semantic graph theory linguistic approach statistical approach

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