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Summarizing Documents in Context: Modeling the User’s Information Need

  • Yllias Chali
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)

Abstract

Popularity of the Internet has contributed towards the explosive growth of the information available to users for day to day usage, and people are faced with information overload problems because of the spread of the information across various kinds of sources – documents, web pages, mails, faxes, manuals, reports, books, etc. In this paper, we present a text summarization system that models the real-world application in which the user would be interested in learning about a sequence of events. Also, we focus on some evaluation procedures.

Keywords

Query Term Magnetic Levitation Broadcast News Model Summary Human Language Technology 
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.
    McKeown, K., Barzilay, R., Chen, J., Elson, D., Evans, D., Klavans, J., Nenkova, A., Schiffman, B., Sigelman, S.: Columbia’s newsblaster: New features and future directions (demo). In: Proceedings of NAACL-HLT 2003, Edmonton, Canada (2003)Google Scholar
  2. 2.
    Radev, D.R., Blair-Goldensohn, S., Zhang, Z., Sundara Raghavan, R.: Newsinessence: A system for domain-independent, real-time news clustering and multi-document summarization. In: Demo Presentation, Human Language Technology Conference, San Diego, CA (2001)Google Scholar
  3. 3.
    Waibel, A., Bett, M., Finke, M., Stiefelhagen, R.: Meeting browser:tracking and summarization meetings. In: Proceedings of the 1998 DARPA Broadcast News Workshop, Lansdowne, Virginia (1998)Google Scholar
  4. 4.
    McKeown, K.R., Jordan, D.A., Hatzivassiloglou, V.: Generating patient-specific summaries of online literature. In: AAAI 1998 Spring Symposium on Intelligent Text Summarization, Stanford University, pp. 34–43 (1998)Google Scholar
  5. 5.
    Hirst, G., DiMarco, C., Hovy, E., Parsons, K.: Authoring and generating health-education documents that are tailored to the needs of the individual patient. In: Proceedings of the Sixth International Conference on User Modeling, Sardinia Italy, pp. 107–118 (1997)Google Scholar
  6. 6.
    Maybury, M., Merlino, A.: Multimedia summaries of broadcast news. In: Maybury, M. (ed.) Multimedia Information Retrieval (1997)Google Scholar
  7. 7.
    Rohall, S.L., Gruen, D., Moody, P., Wattenberg, M., Stern, M., Kerr, B., Stachel, B., Dave, K., Armes, R., Wilcox, E.: Remail: a reinvented email prototype. In: Extended abstracts of the 2004 conference on Human factors and computing systems, pp. 791–792. ACM Press, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Rambow, O., Shrestha, L., Chen, J., Lauridsen, C.: Summarizing email threads. In: Proceedings of HLT-NAACL 2004: Short Papers, Boston, MA (2004)Google Scholar
  9. 9.
    Sparck-Jones, K.: Automatic summarizing: Factors and directions. In: Mani, Maybury (eds.) Advances in Automatic Text Summarization. MIT press, Cambridge (1999)Google Scholar
  10. 10.
    Choi, F.Y.Y.: Advances in domain independent linear text segmentation. In: Proceedings of the 1st North American Chapter of the Association for Computational Linguistics, Seattle, Washington, pp. 26–33 (2000)Google Scholar
  11. 11.
    Reynar, J.: Topic Segmentation: Algorithms and applications. PhD thesis, Computer and Information Science, University of Pennsylvania (1998)Google Scholar
  12. 12.
    Chali, Y., Noureddine, S.: Document clustering with grouping and chaining algorithms. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 280–291. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Chali, Y.: Topic detection of unrestricted texts: Approaches and evaluations. Journal of Applied Artificial Intelligence 19(2), 119–136 (2005)CrossRefGoogle Scholar
  14. 14.
    Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Technical report, Ithaca, NY, USA (1987)Google Scholar
  15. 15.
    Lin, C.Y.: ROUGE: A package for automatic evaluation of summaries. In: Proceedings of the Workshop on Text Summarization Branches Out, Barcelona, Spain, pp. 74–81 (2004)Google Scholar
  16. 16.
    Nenkova, A., Passonneau, R.: Evaluating content selection in summarization: the Pyramid method. In: Proceedings of the Human Language Technology Research Conference/North American Chapter of the Association of COmputation Linguistics, Boston, MA, pp. 145–152 (2004)Google Scholar
  17. 17.
    DUC (ed.): Document Understanding Conference. NIST (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yllias Chali
    • 1
  1. 1.Department of Computer ScienceUniversity of LethbridgeLethbridge, AlbertaCanada

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