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Less Is More: Maximal Marginal Relevance as a Summarisation Feature

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Book cover Advances in Information Retrieval Theory (ICTIR 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5766))

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Abstract

Summarisation approaches aim to provide the most salient concepts of a text in a condensed representation. Repetition of extracted material in the generated summary should be avoided. Carbonell and Goldstein proposed Maximal Marginal Relevance as a measure to increase the diversity of documents retrieved by an IR system, and developed a summariser based on MMR. In this paper, we look at the viability of MMR as a feature in the traditional feature-based summarisation approach proposed by Edmundson.

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References

  1. Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998, pp. 335–336. ACM, New York (1998)

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  2. Goldstein, J., Mittal, V., Carbonell, J., Callan, J.: Creating and evaluating multi-document sentence extract summaries. In: CIKM 2000, pp. 165–172. ACM, New York (2000)

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  3. Lin, C.-Y.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of the ACL 2004 Workshop, Barcelona, Spain, July 2004, pp. 74–81. ACL (2004)

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  4. Tombros, A., Sanderson, M.: Advantages of query biased summaries in information retrieval. In: SIGIR 1998, pp. 2–10. ACM, New York (1998)

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Author information

Authors and Affiliations

  1. Department of Computer Science, Queen Mary, University of London, UK

    Jan Frederik Forst, Anastasios Tombros & Thomas Roelleke

Authors
  1. Jan Frederik Forst
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  2. Anastasios Tombros
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  3. Thomas Roelleke
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Editor information

Editors and Affiliations

  1. Department of Computing Science, Sir Alwyn Williams Building, Lilybank Gardens, University of Glasgow, G12 8QQ, Glasgow, Scotland, UK

    Leif Azzopardi

  2. Microsoft Research Ltd, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, UK

    Gabriella Kazai  & Stephen Robertson  & 

  3. Knowledge Media Institute,, The Open University, MK7 6AA, Milton Keynes, UK

    Stefan Rüger

  4. Microsoft Research Ltd, 7 JJ Thomson Avenue, CB3 0FB, Cambridge, United Kingdom

    Milad Shokouhi  & Emine Yilmaz  & 

  5. School of Computing, The Robert Gordon University, St Andrew Street, AB25 1HG, Aberdeen, UK

    Dawei Song

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© 2009 Springer-Verlag Berlin Heidelberg

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Forst, J.F., Tombros, A., Roelleke, T. (2009). Less Is More: Maximal Marginal Relevance as a Summarisation Feature. In: Azzopardi, L., et al. Advances in Information Retrieval Theory. ICTIR 2009. Lecture Notes in Computer Science, vol 5766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04417-5_37

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  • DOI: https://doi.org/10.1007/978-3-642-04417-5_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04416-8

  • Online ISBN: 978-3-642-04417-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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