A Comprehensive Study of Text Summarization Algorithms

Authors

  • Yash Dhankhar  Computer Science and Engineering, BabaMastnath Engineering College, Rohtak, Haryana, India
  • Indu Bala  Information Systems, Delhi Technological University, Delhi, India
  • Swati Singh  Computer Science and Engineering, Delhi Technological University, Delhi, India
  • Sunil Dalal  Assistant Professor, BGSB University, Rajouri, J&K, India

Keywords:

Text Summarization, KWIC, Semantic and Syntactic, Statistical Technique, Clustering Technique, Natural Language Processing

Abstract

This document provides some minimal guidelines (and requirements) for writing a research paper. Issues related to the contents, originality, contributions, organization, bibliographic information, and writing style are briefly covered. Evaluation criteria and due dates for the research paper are also provided.

References

  1. D.K. Gaikwad and C.N. Mahender “A Review Paper on Text Summarization”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 3, March 2016,154-160
  2. Radev, D. R., Hovy, E., and McKeown, K. (2002) “Introduction to the special issue on summarization.” Computational Linguistics., 28(4):399-408
  3. Luhn, H. P. (1958) “ The automatic creation of literature abstracts”. IBM Journal of Research Development, 2(2):159–165
  4. Edmundson, H. P. (1969) “ New methods in automatic extracting”. Journal of the ACM, 16(2):264–285.
  5. R.Mihalcea, and P.Tarau, “TextRank: Bringing Order into Texts.” In Proceedingsof Empirical Methods in Natural Language Processing (EMNLP). pp. 404-411. 2004.
  6. Z.Pei-ying, and L.Cun-he, “Automatic Text Summarization based on Sentences Clustering and Extraction,” Proceeding of the 2nd IEEE International Conference on Computer Science and Information Technology. pp. 167-170. 2009
  7. 20IOy International Conference on Computer Application and System Modeling (ICCASM 2010) Automatic Text Summarization Based On Rhetorical Structure Theory Li Chengcheng 595-598[8] D. Blei, A. Ng, and M. Jordan “ Latent Dirichlet allocation”. In Journal of Machine Learning Research, 3:993–1022, January2003.
  8. Barzilay, R. and Elhadad, M. (1997). “Using lexical chains for text summarization.” in Proceedings ISTS’97. pg. 38-41
  9. Radev, D. R. and McKeown, K. (1998) “Generating natural language summaries from multiple on-line sources.” Computational Linguistics, 24(3):469–500
  10. S. Banerjee, P.Mitra and K. Sugiyama “ Multi-Document Abstractive Summarization Using ILP Based Multi-Sentence Compression”in Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
  11. Lin, C.-Y. (2004). “Rouge: A package for automatic evaluation of summaries.” In Proceedings of the ACL-04 Workshop, pages 74–81, Barcelona, Spain
  12. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu “BLEU: a Method for Automatic Evaluation of Machine Translation” in Computational Linguistics (ACL), Philadelphia, July 2002, pp. 311-318
  13. S. Brin and L. Page “The PageRank Citation Ranking:Bringing Order to the Web” in 1999
  14. Mc Keown, K. R. and Radev, D. R. (1995). “Generating summaries of multiple news articles.” in Proceedings of SIGIR ’95, pages 74–82, Seattle, Washington.
  15. Jagadeesh J, Prasad Pingali, Vasudeva Varma “Sentence Extraction Based Single Document Summarization” Workshop on Document Summarization, 19th and 20th March, 2005, IIIT Allahabad
  16. Kamal Sarkar, “Sentence Clustering-based Summarization of Multiple Text Documents”, TECHNIA – International Journal of Computing Science and Communication Technologies, vol. 2, no. 1, Jul. 2009.
  17. F. Canan Pembe and Tunga Güngör, “Automated Query-biased and Structure-preserving Text Summarization on Web Documents,” in Proceedings of the International Symposium on Innovations in Intelligent Systems and Applications, İstanbul, June 2007.
  18. Reeve Lawrence H., Han Hyoil, Nagori Saya V., Yang Jonathan C., Schwimmer Tamara A., Brooks Ari D., “Concept Frequency Distribution in Biomedical Text Summarization”, ACM 15th Conference on Information and Knowledge Management (CIKM), Arlington, VA, USA,2006.
  19. Khan Atif, Salim Naomie, “A review on abstractive summarization Methods”, Journal of Theoretical and Applied Information Technology, 2014, Vol. 59
  20. Evans, D. K. (2005). “Similarity-based multilingual multi-document summarization.” Technical Report CUCS-014-05, Columbia University.
  21. Edmundson, H. P. (1969). “New methods in automatic extracting.” Journal of the ACM, 16(2):264–285.
  22. Martins, Camilla Brandel and Lucia Helena Machado Rino. “Revisiting UNLSumm: Improvement Through a Case Study.” (2002).
  23. Conroy, J. M. and O’leary, D. P. (2001). “Text summarization via hidden markov models.” In Proceedings of SIGIR ’01, pages 406–407, New York, NY, USA
  24. Kupiec, J., Pedersen, J., and Chen, F. (1995). “A trainable document summarizer.” In Proceedings SIGIR ’95, pages 68–73, New York, NY, USA.
  25. Aone, C., Okurowski, M. E., Gorlinsky, J., and Larsen, B. (1999). “A trainable summarizer with knowledge acquired from robust nlp techniques”.pages 71–80
  26. Lin, C.-Y. and Hovy, E. (1997). “Identifying topics by position.” In Proceedings of the Fifth conference on Applied natural language processing, pages 283–290, San Francisco, CA, USA.
  27. Osborne, M. (2002). Using maximum entropy for sentence extraction. In Proceedings of the ACL’02 Workshop on Automatic Summarization, pages 1–8, Morristown, NJ, USA
  28. Svore, K., Vanderwende, L., and Burges, C. (2007). “Enhancing single-document summarization by combining RankNet and third-party sources.” In Proceedings of the EMNLP-CoNLL, pages 448–457.
  29. Barzilay, R. and Elhadad, M. (1997). “Using lexical chains for text summarization.” in Proceedings ISTS’97.
  30. Hovy, E. and Lin, C. Y. (1999). “Automated text summarization in summarist.” In Mani, I. and Maybury, M. T., editors, Advances in Automatic Text Summarization, pages 81–94. MIT Press
  31. N. Aletras and M. Stevenson. “Evaluating topic coherence using distributional semantics.” In Proc. Of the 10th Int. Conf. on Computational Semantics (IWCS’13), pages 13–22, 2013.
  32. Kamal Sarkar “Automatic Single Document Text Summarization Using Key Concepts in Documents” J Inf Process Syst, Vol.9, No.4, pp.602-620, December 2013
  33. I. Chen “Integer Linear Programming Models for Constrained Clustering” in International Conference on Discovery Science 2010: Discovery Science pp 159-173
  34. Günes Erkan and Dragomir R. Radev. 2004. “LexRank: graph-based lexical centrality as salience in text summarization”. J. Artif. Int. Res. 22, 1 (December 2004), 457-479.
  35. Jinqiang Bian, Zengru Jiang, Qian Chen 2014 “Research On Multi-document Summarization Based On LDA Topic Model” Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics 113-116
  36. Virendra Kumar Gupta Tanveer J. Siddiqui “Multi-Document Summarization Using Sentence Clustering” IEEE Proceedings of 4th International Conference on Intelligent Human Computer Interaction, Kharagpur, India, December 27-29, 2012
  37. The Porter Stemming Algorithm [Online] Available:http://tartarus.org/~martin/PorterStemmer
  38. George A. Miller. “WordNet: A Lexical Database for English.” Communications of the ACM, pages 39-41, November 1995
  39. Sherry and Dr. P. Bhatia “ A Survey to Automatic Text Summarization Techniques” International Journal of Engineering Reasearch, October 2015 Pg. 1045- 1053
  40. Chin-Yew Lin and Eduard Hovy, “Identifying Topics by Position,” In Proceedings of the Fifth conference on Applied natural language processing, San Francisco, pp. 283-290, 1997.
  41. S. P. Yong, A. I. Z. Abidin and Y. Y. Chen, “A Neural Based Text Summarization System,” 6th International Conference of Data Mining, pp. 45-50, 2005.
  42. Ruqaiya Hasan, Coherence and Cohesive Harmony, In: Flood James (Ed.), Understanding Reading Comprehension: Cognition, Language and the Structure of Prose. Newark, Delaware: International Reading Association, pp. 181-219, 1984.
  43. William C. Mann and Sandra A. Thompson, Relational Propositions in Discourse, Defense Technical Information Center,
  44. Branimir Boguraev and Christopher Kennedy, “Saliencebased Content Characterization of Text Documents,” In Proceedings of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization, 1997.
  45. Li Chengcheng, “Automatic Text Summarization Based On Rhetorical Structure Theory,” International Conference on Computer Application and System Modeling (ICCASM), vol. 13, pp. 595-598, October 2010.
  46. Xiaojun Wan, “An Exploration of Document Impact on Graph-Based Multi-Document Summarization,” Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics,
  47. Tiedan Zhu and Xinxin Zhao, “An Improved Approach to Sentence Ordering For Multi-document Summarization,” IACSIT Hong Kong Conferences, IACSIT Press, Singapore, vol. 25, pp. 29-33, 2012.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-04-25

Issue

Volume 4, Issue 1, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Yash Dhankhar, Indu Bala, Swati Singh, Sunil Dalal, " A Comprehensive Study of Text Summarization Algorithms, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 1, pp.34-41, March-April-2018.