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Event-Based Summarization Using Time Features

  • Mingli Wu
  • Wenjie Li
  • Qin Lu
  • Kam-Fai Wong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4394)

Abstract

We investigate whether time features help to improve event-based summarization. In this paper, events are defined as event terms and the associated event elements. While event terms represent the actions themselves, event elements denote action arguments. After anchoring events on the time line, two different statistical measures are employed to identify importance of events on each day. Experiments show that the combination of tf*idf weighting scheme and time features can improve the quality of summaries significantly. The improvement can be attributed to its capability to represent the trend of news topics depending on event temporal distributions.

Keywords

Time Slot Reference Time Event Element Time Line Computational Linguistics 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Afantenos, S.D., Karkaletsis, V., Stamatopoulos, P.: Summarizing Reports on Evolving Events; Part I: Linear Evolution. In: Proceedings of Recent Advances in Natural Language Processing (2005)Google Scholar
  2. 2.
    Allan, J., Gupta, R., Khandelwal, V.: Temporal Summaries of News Topics. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 10–18. ACM Press, New York (2001)Google Scholar
  3. 3.
    Allen, J.F.: An Interval-based Representation of Temporal Knowledge. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp. 221–226 (1981)Google Scholar
  4. 4.
    Brandow, R., Mitze, K., Rau, L.F.: Automatic Condensation of Electronic Publications by Sentence Selection. Information Processing and Management 31(5), 675–686 (1995)CrossRefGoogle Scholar
  5. 5.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335–336. ACM Press, New York (1998)Google Scholar
  6. 6.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: an Architecture for Development of Robust HLT Applications. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (2002)Google Scholar
  7. 7.
    Edmundson, H.P.: New Methods in Automatic Extracting. Journal of the Association for Computing Machinery 16(2), 264–285 (1969)CrossRefMATHGoogle Scholar
  8. 8.
    Filatova, E., Hatzivassiloglou, V.: Event-based extractive summarization. In: Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics Workshop, pp. 104–111 (2004)Google Scholar
  9. 9.
    Jatowt, A., Ishizuka, M.: Temporal Web Page Summarization. In: Proceedings of the 5th International Conference on Web Information Systems Engineering, pp. 303–312 (2004)Google Scholar
  10. 10.
    Kupiec, J., Pedersen, J., Chen, F.: A trainable document summarizer. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 68–73. ACM Press, New York (1995)Google Scholar
  11. 11.
    Lim, J.-M., Kang, I.-S., Bae, J.-H., Lee, J.-H.: Sentence Extraction Using Time Features in Multi-document Summarization. In: Information Retrieval Technology: Asia Information Retrieval Symposium (2004)Google Scholar
  12. 12.
    Luhn, H.P.: The Automatic Creation of Literature Abstracts. IBM Journal of Research and Development 2, 159–165 (1958)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Mani, I., Wilson, G.: Robust Temporal Processing of News. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (2000)Google Scholar
  14. 14.
    Radev, D.R., Allison, T., et al.: MEAD - a Platform for Multidocument Multilingual Text Summarization. In: Proceedings of 4th International Conference on Language Resources and Evaluation (2004)Google Scholar
  15. 15.
    Schilder, F., Habel, C.: From Temporal Expressions to Temporal Information: Semantic Tagging of News Messages. In: Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics, Workshop on Temporal and Spatial Information Processing, pp. 65–72 (2001)Google Scholar
  16. 16.
    Swan, R., Allan, J.: Automatic Generation of Overview Timelines. In: Proceedings of the 23th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 49–56. ACM Press, New York (2000)Google Scholar
  17. 17.
    Teufel, S., Moens, M.: Argumentative Classification of Extracted Sentences as a First Step towards Flexible Abstracting. In: Mani, I., Maybury, M.T. (eds.) Advances in Automatic Text Summarization, pp. 137–154. MIT Press, Cambridge (1999)Google Scholar
  18. 18.
    Vanderwende, L., Banko, M., Menezes, A.: Event-Centric Summary Generation (2004), Available at, http://duc.nist.gov/pubs.html
  19. 19.
    Wu, M.: Investigation on Event-Based Summarization. In: Proceedings of the COLING/ACL 2006 Student Research Workshop (2006)Google Scholar
  20. 20.
    Yoshioka, M., Haraguchi, M.: Multiple News Articles Summarization Based on Event Reference Information. In: Working Notes of the 4th NTCIR Workshop Meeting (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mingli Wu
    • 1
  • Wenjie Li
    • 1
  • Qin Lu
    • 1
  • Kam-Fai Wong
    • 2
  1. 1.Department of Computing, The Hong Kong Polytechnic University, KowloonHong Kong
  2. 2.Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T.Hong Kong

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