Knowledge and Information Systems

, Volume 50, Issue 3, pp 945–968 | Cite as

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

  • Luís Marujo
  • Ricardo Ribeiro
  • Anatole Gershman
  • David Martins de Matos
  • João P. Neto
  • Jaime Carbonell
Regular Paper
  • 295 Downloads

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.

Keywords

Event detection Extractive summarization Passage retrieval Automatic key phrase extraction Centrality 

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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Luís Marujo
    • 1
  • Ricardo Ribeiro
    • 2
    • 3
  • Anatole Gershman
    • 4
  • David Martins de Matos
    • 2
    • 5
  • João P. Neto
    • 2
    • 5
  • Jaime Carbonell
    • 4
  1. 1.Feedzai ResearchLisbonPortugal
  2. 2.INESC-ID LisboaLisbonPortugal
  3. 3.Instituto Universitário de Lisboa (ISCTE-IUL)LisbonPortugal
  4. 4.School of Computer Science, CMUPittsburghUSA
  5. 5.Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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