Skip to main content
Springer Nature Link
Log in

Swarm Diversity Based Text Summarization

  • Conference paper
Neural Information Processing (ICONIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5864))

Included in the following conference series:

Abstract

Automatic text summarization systems aim to make their created summaries closer to human summaries. The summary creation under the condition of the redundancy and the summary length limitation is a challenge problem. The automatic text summarization system which is built based on exploiting of the advantages of different techniques in form of an integrated model could produce a good summary for the original document. In this paper, we introduced an integrated model for automatic text summarization problem; we tried to exploit different techniques advantages in building of our model like advantage of diversity based method which can filter the similar sentences and select the most diverse ones and advantage of the differentiation between the most important features and less important using swarm based method. The experimental results showed that our model got the best performance over all methods used in this study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2(92), 159–165 (1958)

    Article  MathSciNet  Google Scholar 

  2. Baxendale, P.: Machine-made index for technical literature - an experiment. IBM Journal of Research Development 2(4), 354–361 (1958)

    Google Scholar 

  3. Edmundson, H.P.: New methods in automatic extracting. Journal of the Association for Computing Machinery 16(2), 264–285 (1969)

    MATH  Google Scholar 

  4. Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the ACM SIGIR conference, New York, USA, July 1995, pp. 68–73 (1995)

    Google Scholar 

  5. Lin, C.Y., Hovy, E.: Identifying topics by position. In: Proceedings of the Fifth conference on applied natural language processing, San Francisco, CA, USA, March 1997, pp. 283–290 (1997)

    Google Scholar 

  6. Lin, C.Y.: Training a selection function for extraction. In: Proceedings of the Eighteenth Annual International ACM Conference on Information and Knowledge Management (CIKM), Kansas City, Kansas, November 2-6, pp. 55–62 (1999)

    Google Scholar 

  7. Conroy, J.M., O’leary, D.P.: Text summarization via hidden markov models. In: Proceedings of SIGIR 2001, September 9-12, 2001, pp. 406–407 (2001)

    Google Scholar 

  8. Osborne, M.: Using maximum entropy for sentence extraction. In: Proceedings of the ACL 2002 Workshop on Automatic Summarization, Morristown, NJ, USA, July 2002, pp. 1–8 (2002)

    Google Scholar 

  9. Svore, K., Vanderwende, L., Burges, C.: Enhancing single-document summarization by combining RankNet and third-party sources. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, June 2007, pp. 448–457. Association for Computational Linguistics, Prague (2007)

    Google Scholar 

  10. Fattah, M.A., Ren, F.: GA, MR, FFNN, PNN and GMM based models for automatic text summarization. Computer Speech and Language 23(1), 126–144 (2008)

    Article  Google Scholar 

  11. Ono, K., Sumita, K., Miike, S.: Abstract generation based on rhetorical structure extraction. In: Proceedings of 15th International Conference on Computational Linguistics (COLING 1994), Kyoto, August 5-9, pp. 344–348 (1994)

    Google Scholar 

  12. Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. In: Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS 1997), August 1997, pp. 10–17. ACL, Madrid (1997)

    Google Scholar 

  13. Marcu, D.: Improving summarization through rhetorical parsing tuning. In: Proceedings of The Sixth Workshop on Very Large Corpora, Montreal, Canada, August 1998, pp. 206–215 (1998a)

    Google Scholar 

  14. Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, August 24-28, 1998, pp. 335–336 (1998)

    Google Scholar 

  15. Kraaij, W., Spitters, M., Heijden, M.v.d.: Combining a mixture language model and naive bayes for multi-document summarization. In: Proceedings of Document Understanding Conference, New Orleans, LA, September 13-14, pp. 109–116 (2001)

    Google Scholar 

  16. Mori, T., Nozawa, M., Asada, Y.: Multi-Answer-Focused Multi-document Summarization Using a Question-Answering Engine. ACM Transactions on Asian Language Information Processing 4(3), 305–320 (2005)

    Article  Google Scholar 

  17. Liu, D., Wang, Y., Liu, C., Wang, Z.: Multiple Documents Summarization Based on Genetic Algorithm. In: Wang, L., et al. (eds.) Fuzzy Systems and Knowledge Discovery, pp. 355–364. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  18. Zajic, D.M., Dorr, B.J., Schwartz, R., Lin, J.: Sentence Compression as a Component of a Multi-Document Summarization System. In: Proceedings of the 2006 Document Understanding Workshop, June 8-9, New York (2006)

    Google Scholar 

  19. Filippova, K., Mieskes, M., Nastase, V., Ponzetto, S.P., Strube, M.: Cascaded Filtering for Topic-Driven Multi-Document Summarization. In: Proceedings of the Document Understanding Conference, Rochester, N.Y., April 26-27, pp. 30–35 (2007)

    Google Scholar 

  20. Ye, S., Qiu, L., Chua, T., Kan, M.: NUS at DUC 2005: Understanding documents via concept links. In: Proceedings of Document Understanding Conference, Vancouver, Canada, October 9-10 (2005)

    Google Scholar 

  21. Lin, Z., Chua, T., Kan, M., Lee, W., Sun, Q.L., Ye, S.: NUS at DUC 2007: Using Evolutionary Models of Text. In: Proceedings of Document Understanding Conference, Rochester, NY, USA, April 26-27 (2007)

    Google Scholar 

  22. Aretoulaki, M.: Towards a Hybrid Abstract Generation System. In: Int. Conf. on New Methods in Language Processing, Manchester, pp. 220–227 (1994)

    Google Scholar 

  23. Alemany, A.L., Fort, M.F.: Integrating cohesion and coherence for Automatic Summarization. In: EACL 2003 Student Session, ACL, Budapest, pp. 1–8 (2003)

    Google Scholar 

  24. da Cunha, I., Fernández, S., Velázquez Morales, P., Vivaldi, J., SanJuan, E., Torres-Moreno, J.-M.: A new hybrid summarizer based on vector space model, statistical physics and linguistics. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 872–882. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  25. Binwahlan, M.S., Salim, N., Suanmali, L.: MMI Diversity Based Text Summarization. IJCSS International Journal of Computer Science and Security 3(1), 23–33 (2009)

    Google Scholar 

  26. Binwahlan, M.S., Salim, N., Suanmali, L.: Swarm Based Text Summarization. In: International Conference on IACSIT Spring Conference, Singapore, April 17-20, pp. 145–150 (2009)

    Google Scholar 

  27. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm, Systems Man, and Cybernetics. In: IEEE International Conference on Computational Cybernetics and Simulation, New York, vol. 5, pp. 4104–4108 (1997)

    Google Scholar 

  28. NIST, The Document Understanding Conference (DUC). (2002), http://duc.nist.gov

  29. Lin, C.: Rouge: A package for automatic evaluation of summaries. In: Proceedings of the Workshop on Text Summarization Branches Out, 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, July 25-26, pp. 74–81 (2004b)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Mohammed Salem Binwahlan & Naomie Salim

  2. Faculty of Science and Technology, Suan Dusit Rajabhat University, 295 Ratchasrima Rd, Dusit, Bangkok, Thailand, 10300

    Ladda Suanmali

Authors
  1. Mohammed Salem Binwahlan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naomie Salim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ladda Suanmali
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Hong Kong,  

    Chi Sing Leung

  2. School of Electrical Engineering and Computer Science, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, 702-701, Taegu, Korea

    Minho Lee

  3. School of Information Technology, King Mongkut’s University of Technology Thonburi, 126 Pracha-U-Thit Rd., Bangmod, Thungkru, 10140, Bangkok, Thailand

    Jonathan H. Chan

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Binwahlan, M.S., Salim, N., Suanmali, L. (2009). Swarm Diversity Based Text Summarization. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10684-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10682-8

  • Online ISBN: 978-3-642-10684-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics