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Event-based summarization using a centrality-as-relevance model

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Abstract

Event detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint method, which is able to detect all types of events in the ACE 2005 Multilingual Corpus. Our base summarization approach is a two-stage method that starts by extracting a collection of key phrases that will be used to help the centrality-as-relevance retrieval model. We explored three different ways to integrate event information, achieving state-of-the-art results in text and speech corpora: (1) filtering of nonevents, (2) event fingerprints as features, and (3) combination of filtering of nonevents and event fingerprints as features.

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Notes

  1. https://news.google.com/.

  2. See http://duc.nist.gov/duc2004/tasks.html.

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Acknowledgments

We thank anonymous reviewers for their very useful comments and suggestions. This work was supported by national funds through FCT under Project UID/CEC/50021/2013, the Carnegie Mellon Portugal Program, and Grant SFRH/BD/33769/2009.

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Authors and Affiliations

  1. Feedzai Research, Lisbon, Portugal

    Luís Marujo

  2. INESC-ID Lisboa, Lisbon, Portugal

    Ricardo Ribeiro, David Martins de Matos & João P. Neto

  3. Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal

    Ricardo Ribeiro

  4. School of Computer Science, CMU, Pittsburgh, USA

    Anatole Gershman & Jaime Carbonell

  5. Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    David Martins de Matos & João P. Neto

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  1. Luís Marujo
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Correspondence to Luís Marujo.

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Marujo, L., Ribeiro, R., Gershman, A. et al. Event-based summarization using a centrality-as-relevance model. Knowl Inf Syst 50, 945–968 (2017). https://doi.org/10.1007/s10115-016-0966-4

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