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Multilingual Statistical News Summarization

Part of the Theory and Applications of Natural Language Processing book series (NLP)

Abstract

In this chapter we present a generic approach for summarizing clusters of multilingual news articles such as the ones produced by the Europe Media Monitor (EMM) system. Our approach uses robust statistical techniques as well as multilingual tools for named entity recognition and disambiguation to produce entity-centered summaries. We run experiments with the TAC 2008 and 2009 data sets (English corpora for summarization research), and we obtained very promising results; at TAC 2009 our runs attained top rank for linguistic quality and second best for overall responsiveness. We also run a small-scale evaluation on languages other than English, demonstrating thereby the multilinguality of our approach, but also providing interesting evidence that contradicts the pervasive assumption “if it works for English, it works for any language”. Finally, we present an online system currently under development which will eventually incorporate all the elements of the summarization approach discussed hereby and we show sample output summaries in various languages.

Keywords

  • News Article
  • Latent Semantic Analysis
  • Entity Recognition
  • Name Entity
  • Trigger Word

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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Fig. 11.1
Fig. 11.2
Fig. 11.3

Notes

  1. 1.

    http://www.nist.gov/tac

  2. 2.

    http://www.project-syndicate.org/

  3. 3.

    http://www.project-syndicate.org/

  4. 4.

    The multilingual MeSH term recognition software was developed by Health-on-the-Net HON, see http://www.hon.ch/

  5. 5.

    The use of the multilingual tools in higher-level applications can be seen at http://emm.newsexplorer.eu/

  6. 6.

    The Medical Subject Headings (MeSH) thesaurus is prepared by the US National Library of Medicine for indexing, cataloging, and searching for biomedical and health-related information and documents. Although, it was initially meant for biomedical and health-related documents, since it represent a large IS-A taxonomy it can be used in more general tasks (http://www.nlm.nih.gov/mesh/meshhome.html)

  7. 7.

    Steinberger et al. [33] worked on monolingual single-document summarization.

  8. 8.

    The multilingual named entity disambiguator and geo-tagger developed at the JRC have already been used for cross-lingual linking of multilingual news clusters produced by the EMM system [34].

  9. 9.

    Note that the statistical test we used to attest significance was ran against the improved version of the lexical-only summarizer and not the official TAC-08 scores, since we considered it was the fairer comparison.

  10. 10.

    The purpose of the update summarization task is to produce a summary of only the novel information contained in a newer set of news articles with respect to an older set, both covering the same news story.

  11. 11.

    For more details on EMM see [1].

  12. 12.

    Online demo of the system is available at http://emm-labs.jrc.it/EMMLabs/NewsGist.html

  13. 13.

    http://tomcat.apache.org/

  14. 14.

    http://code.google.com/p/matrix-toolkits-java/

  15. 15.

    http://math.nist.gov/javanumerics/jama/

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Acknowledgements

We would like to thank the EMM team for providing a stable and robust news gathering infrastructure.

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Correspondence to Mijail Kabadjov .

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Kabadjov, M., Steinberger, J., Steinberger, R. (2013). Multilingual Statistical News Summarization. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28569-1_11

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