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Open-Domain Multi-Document Summarization via Information Extraction: Challenges and Prospects

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Multi-source, Multilingual Information Extraction and Summarization

Abstract

Information Extraction (IE) and Summarization share the same goal of extracting and presenting the relevant information of a document. While IE was a primary element of early abstractive summarization systems, it’s been left out in more recent extractive systems. However, extracting facts, recognizing entities and events should provide useful information to those systems and help resolve semantic ambiguities that they cannot tackle. This paper explores novel approaches to taking advantage of cross-document IE for multi-document summarization. We propose multiple approaches to IE-based summarization and analyze their strengths and weaknesses. One of them, re-ranking the output of a high performing summarization system with IE-informed metrics, leads to improvements in both manually-evaluated content quality and readability.

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Notes

  1. 1.

    http://www.nist.gov/speech/tests/ace/

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Acknowledgements

The first author and the third author were supported by the U.S. Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053, the U.S. NSF CAREER Award under Grant IIS-0953149 and PSC-CUNY Research Program. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Author information

Authors and Affiliations

  1. Computer Science Department, Queens College and Graduate Center, City University of New York, New York, NY, USA

    Heng Ji & Wen-Pin Lin

  2. LIF, Aix-Marseille Université, Marseille, France

    Benoit Favre

  3. Computer Science Department, University of California, Berkeley, CA, USA

    Dan Gillick

  4. Speech Labs, Microsoft, Mountain View, CA, USA

    Dilek Hakkani-Tur

  5. Computer Science Department, New York University, New York, NY, USA

    Ralph Grishman

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  1. Heng Ji
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  2. Benoit Favre
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  3. Wen-Pin Lin
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  4. Dan Gillick
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  5. Dilek Hakkani-Tur
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  6. Ralph Grishman
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Corresponding author

Correspondence to Heng Ji .

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Editors and Affiliations

  1. Universite Sorbonne Nouvelle, LATTICE-CNRS, Ecole Normale Superieure and, rue d'Ulm 45, Paris, 75005, France

    Thierry Poibeau

  2. , Information & Communication Technologies, Universitat Pompeu Fabra, C/ Tanger 122-140, Barcelona, 08018, Spain

    Horacio Saggion

  3. Institute for Computer Science, Polish Acadmey of Science, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland

    Jakub Piskorski

  4. Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2, Helsinki, 00014, Finland

    Roman Yangarber

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Ji, H., Favre, B., Lin, WP., Gillick, D., Hakkani-Tur, D., Grishman, R. (2013). Open-Domain Multi-Document Summarization via Information Extraction: Challenges and Prospects. 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_9

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  • DOI: https://doi.org/10.1007/978-3-642-28569-1_9

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