NewsGist: A Multilingual Statistical News Summarizer

  • Mijail Kabadjov
  • Martin Atkinson
  • Josef Steinberger
  • Ralf Steinberger
  • Erik van der Goot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6323)

Abstract

In this paper we present NewsGist, a multilingual, multi-document news summarization system underpinned by the Singular Value Decomposition (SVD) paradigm for document summarization and purpose-built for the Europe Media Monitor (EMM). The summarization method employed yielded state-of-the-art performance for English at the Update Summarization task of the last Text Analysis Conference (TAC) 2009 and integrated with EMM represents the first online summarization system able to produce summaries for so many languages. We discuss the context and motivation for developing the system and provide an overview of its architecture. The paper is intended to serve as accompaniment of a live demo of the system, which can be of interest to researchers and engineers working on multilingual open-source news analysis and mining.

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References

  1. 1.
    Atkinson, M., Van der Goot, E.: Near real-time information mining in multilingual news. In: Proceedings of the 18th International World Wide Web Conference (WWW 2009), Madrid, Spain, pp. 1153–1154 (April 2009)Google Scholar
  2. 2.
    Kabadjov, M., Steinberger, J., Pouliquen, B., Steinberger, R., Poesio, M.: Multilingual statistical news summarisation: Preliminary experiments with English. In: Proceedings of IAPWNC at the IEEE/WIC/ACM WI-IAT (2009)Google Scholar
  3. 3.
    Piskorski, J.: CORLEONE - core linguistic entity online extraction. Tech. Rep. EN 23393, Joint Research Centre of the European Commission (2008)Google Scholar
  4. 4.
    Pouliquen, B., Steinberger, R.: Automatic construction of multilingual name dictionaries. In: Goutte, C., Cancedda, N., Dymetman, M., Foster, G. (eds.) Learning Machine Translation. NIPS series, MIT Press, Cambridge (2009)Google Scholar
  5. 5.
    Spärck-Jones, K.: Automatic summarising: Factors and directions. In: Mani, I., Maybury, M. (eds.) Advances in Automatic Text Summarization. MIT Press, Cambridge (1999)Google Scholar
  6. 6.
    Steinberger, J., Kabadjov, M., Pouliquen, B., Steinberger, R., Poesio, M.: WB-JRC-UT’s participation in TAC 2009: Update Summarization and AESOP tasks. In: National Institute of Standards and Technology (eds.) Proceedings of TAC, Gaithersburg, MD (November 2009)Google Scholar
  7. 7.
    Steinberger, R., Pouliquen, B., Ignat, C.: Using language-independent rules to achieve high multilinguality in text mining. In: Fogelman-Soulié, F., Perrotta, D., Piskorski, J., Steinberger, R. (eds.) Mining Massive Data Sets for Security. IOS-Press, Amsterdam (2009)Google Scholar
  8. 8.
    Steinberger, R., Pouliquen, B., van der Goot, E.: An introduction to the europe media monitor family of applications. In: Gey, F., Kando, N., Karlgren, J. (eds.) Proceeding of the SIGIR Workshop on Information Access in a Multilingual World (SIGIR-CLIR 2009), Boston, USA (July 2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mijail Kabadjov
    • 1
  • Martin Atkinson
    • 1
  • Josef Steinberger
    • 1
  • Ralf Steinberger
    • 1
  • Erik van der Goot
    • 1
  1. 1.Joint Research Centre, European CommissionIspra (VA)Italy

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