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Coreference Applications to Summarization

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Book cover Anaphora Resolution

Abstract

In this chapter we discuss the connection between anaphora/coreference resolution and summarization. The discussion follows the summarization framework based on Latent Semantic Analysis (LSA), however, the ideas can be applied to any sentence-scoring approach. After describing the ways of combining basic (lexical) features of the summarizer with those received from the coreference resolution system we try to answer the question whether coreference resolution helps to improve the quality of selected content even if coreference resolution systems are still far from perfect. Both single-document and multi-document summarization branches are discussed. Then we focus on post-processing techniques to improve the referential clarity and coherence of extracted summaries.

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Notes

  1. 1.

    We rather use the term ‘coreference resolution’ as a more general term to anaphora resolution. However, when we discuss single-document summarization the term refers to the task of identifying successive mentions of the same discourse entity (intra-document coreference resolution/anaphora resolution), as opposed to the task of ‘inter-document coreference resolution’ appropriate in the case of multi-document summarization which involves collecting all information about an entity, including information expressed by appositions and other predicative constructions.

  2. 2.

    The National Institute of Standards and Technology (NIST) initiated the Document Understanding Conference (DUC) series [11] to evaluate automatic text summarization. Its goal is to further progress in summarization and enable researchers to participate in large-scale experiments. Since 2008 DUC has moved to TAC (Text Analysis Conference) [52] that follows the summarization evaluation roadmap with new or upgraded tracks.

  3. 3.

    When producing an update summary of a set of topic-related documents the summarizer assumes prior knowledge of the reader determined by a set of older documents of the same topic. The update summarizer thus must solve a novelty vs. redundancy problem.

  4. 4.

    In the aspect summarization scenario a given list of core information aspects for different event types should be addressed in the automatic summaries.

  5. 5.

    In many non-educational texts only an ‘entity-centered’ structure can be clearly identified, as opposed to a ‘relation-centered’ structure of the type hypothesized in Rhetorical Structures Theory and which serves as the basis for discourse structure-based summarization methods [21, 40].

  6. 6.

    The approach deals with object coreference and event coreference. They further consider the issue of referential relations beyond the identity relation covering a few domain-specific special cases.

  7. 7.

    Available as open source software at http://guitar-essex.sourceforge.net/.

  8. 8.

    This step includes heuristic methods for guessing agreement features.

  9. 9.

    All system summaries were truncated to 100 words as traditionally done in DUC. ROUGE version and settings:

    $$\displaystyle{\mathtt{ROUGEeval-1.4.2.pl-c95-m-n2-l100-s-24-aduc.xml.}}$$
  10. 10.

    The previous sentence in the source is: “Walton continued talking with customers during the concert.”

  11. 11.

    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 [50].

  12. 12.

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

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Acknowledgements

This work was supported by project “NTIS - New Technologies for Information Society”, European Centre of Excellence, CZ.1.05/1.1.00/02.0090, and by project MediaGist, EU’s FP7 People Programme (Marie Curie Actions), no. 630786.

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Authors and Affiliations

  1. Faculty of Applied Sciences, Department of Computer Science and Engineering, NTIS Centre, University of West Bohemia, Univerzitni 8, Pilsen, 306 14, Czech Republic

    Josef Steinberger

  2. School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK

    Mijail Kabadjov & Massimo Poesio

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  1. Josef Steinberger
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Correspondence to Mijail Kabadjov .

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  1. Trento, Italy

    Massimo Poesio

  2. Frankfurt am Main, Germany

    Roland Stuckardt

  3. Heidelberg, Germany

    Yannick Versley

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Steinberger, J., Kabadjov, M., Poesio, M. (2016). Coreference Applications to Summarization. In: Poesio, M., Stuckardt, R., Versley, Y. (eds) Anaphora Resolution. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47909-4_15

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  • DOI: https://doi.org/10.1007/978-3-662-47909-4_15

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