Updating Users about Time Critical Events

  • Qi Guo
  • Fernando Diaz
  • Elad Yom-Tov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7814)


During unexpected events such as natural disasters, individuals rely on the information generated by news outlets to form their understanding of these events. This information, while often voluminous, is frequently degraded by the inclusion of unimportant, duplicate, or wrong information. It is important to be able to present users with only the novel, important information about these events as they develop. We present the problem of updating users about time critical news events, and focus on the task of deciding which information to select for updating users as an event develops. We propose a solution to this problem which incorporates techniques from information retrieval and multi-document summarization and evaluate this approach on a set of historic events using a large stream of news documents. We also introduce an evaluation method which is significantly less expensive than traditional approaches to temporal summarization.


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  1. 1.
    Allan, J. (ed.): Topic Detection and Tracking. Springer (2002)Google Scholar
  2. 2.
    Allan, J., Gupta, R., Khandelwal, V.: Temporal summaries of new topics. In: Proceedings of the 24th ACM SIGIR, pp. 10–18 (2001)Google Scholar
  3. 3.
    Balasubramanian, N., Cucerzan, S.: Topic pages: An alternative to the ten blue links. In: Fourth IEEE International Conference on Semantic Computing (2010)Google Scholar
  4. 4.
    Dang, H.T., Owczarzak, K.: Overview of the TAC 2008 Update Summarization Task, pp. 1–16 (2008)Google Scholar
  5. 5.
    Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Int. Res. 22, 457–479 (2004)Google Scholar
  6. 6.
    Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Marie-Francine Moens, S.S. (ed.) Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81 (July 2004)Google Scholar
  7. 7.
    Mendoza, M., Poblete, B., Castillo, C.: Twitter under crisis: can we trust what we rt? In: Proceedings of the First Workshop on Social Media Analytics (2010)Google Scholar
  8. 8.
    Nenkova, A., McKeown, K.: Automatic summarization. Foundations and Trends in Information Retrieval 5(2-3) (2011)Google Scholar
  9. 9.
    Nenkova, A., Vanderwende, L., McKeown, K.: A compositional context sensitive multi-document summarizer. In: Proceedings of the 29th ACM SIGIR (2006)Google Scholar
  10. 10.
    Olsson, U., Drasgow, F., Dorans, N.: The polyserial correlation coefficient. Psychometrika 47(3), 337–347 (1982)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Ouyang, Y., Li, W., Li, S., Lu, Q.: Applying regression models to query-focused multi-document summarization. Info. Processing and Management 47(2) (2011)Google Scholar
  12. 12.
    Sauper, C., Barzilay, R.: Automatically generating wikipedia articles: a structure-aware approach. In: ACL 2009, pp. 208–216 (2009)Google Scholar
  13. 13.
    Wang, D., Li, T.: Document update summarization using incremental hierarchical clustering. In: Proceedings of the 19th ACM CIKM (2010)Google Scholar
  14. 14.
    Yom-Tov, E., Diaz, F.: Out of sight, not out of mind: on the effect of social and physical detachment on information need. In: Proceedings of the 34th ACM SIGIR (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qi Guo
    • 1
  • Fernando Diaz
    • 2
  • Elad Yom-Tov
    • 3
  1. 1.Microsoft CorporationUSA
  2. 2.Microsoft ResearchNew YorkUSA
  3. 3.Microsoft ResearchHerzliyaIsrael

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