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Text Summarization and Singular Value Decomposition

  • Josef Steinberger
  • Karel Ježek
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3261)

Abstract

In this paper we present the usage of singular value decomposition (SVD) in text summarization. Firstly, we mention the taxonomy of generic text summarization methods. Then we describe principles of the SVD and its possibilities to identify semantically important parts of a text. We propose a modification of the SVD-based summarization, which improves the quality of generated extracts. In the second part we propose two new evaluation methods based on SVD, which measure content similarity between an original document and its summary. In evaluation part, our summarization approach is compared with 5 other available summarizers. For evaluation of a summary quality we used, apart from a classical content-based evaluator, both newly developed SVD-based evaluators. Finally, we study the influence of the summary length on its quality from the angle of the three evaluation methods mentioned.

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References

  1. 1.
    Gong, Y., Liu, X.: Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis. In: Proceedings of the 24th ACM SIGIR conference on Research and development in information retrieval, New Orleans, Louisiana, United States, pp. 19–25 (2001)Google Scholar
  2. 2.
    Radev, R., Teufel, S., Saggion, H., Lam, W., Blitzer, J., Qi, H., Celebi, A., Liu, D., Drabek, E.: Evaluation Challenges in Large-scale Document Summarization. In: Proceeding of the 41st meeting of the Association for Computational Linguistics, Sapporo, Japan, pp. 375–382 (2003)Google Scholar
  3. 3.
    Hynek, J., Ježek, K.: Practical Approach to Automatic Text Summarization. In: Proceedings of the ELPUB 2003 conference, Guimaraes, Portugal, pp. 378–388 (2003)Google Scholar
  4. 4.
    Berry, M.W., Dumais, S.T., O’Brien, G.W.: Using Linear Algebra for Intelligent Information Retrieval. SIAM Review (1995)Google Scholar
  5. 5.
    Kupiec, J., Pedersen, J., Chen, F.: A trainable Document Summarizer. In: Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, Washington, United States, pp. 68–73 (1995)Google Scholar
  6. 6.
    Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS 1997), ACL Madrid, Spain (1997)Google Scholar
  7. 7.
    Luhn, H.P.: Automatic Creation of Literature Abstracts. IBM Journal and Research Development 2(2), 159–165 (1958)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Marcu, D.: From Discourse Structures to Text Summaries. In: Proceedings of the ACL 1997/EACL 1997 Workshop on Intelligent Scalable Text Summarization, Madrid, Spain, pp. 82–88 (1997)Google Scholar
  9. 9.
    Jones, P.A., Paice, C.D.: A ‘select and generate’ Approach to Automatic Abstracting. In: Proceeding of the 14th British Computer Society Information Retrieval Colloquium, pp. 151–154. Springer, Heidelberg (1992)Google Scholar
  10. 10.
    Jing, H., McKeown, K.: Cut and Paste Text Summarization. In: Proceedings of the 1st meeting of the North Americat Chapter of the Association for Computational Linguistics, Seattle, Washington, USA, pp. 178–185 (2000)Google Scholar
  11. 11.
    Ono, K., Sumita, K., Miike, S.: Abstract Generation Based on Rhetorical Structure Extraction. In: Proceedings of the 15th International Conference on Computational Linguistics, Kyoto, Japan, pp. 344–348 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Josef Steinberger
    • 1
  • Karel Ježek
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of West Bohemia in PilsenPlzeňCzech Republic

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