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Key Phrase Extraction of Lightly Filtered Broadcast News

  • Luís Marujo
  • Ricardo Ribeiro
  • David Martins de Matos
  • João P. Neto
  • Anatole Gershman
  • Jaime Carbonell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7499)

Abstract

This paper explores the impact of light filtering on automatic key phrase extraction (AKE) applied to Broadcast News (BN). Key phrases are words and expressions that best characterize the content of a document. Key phrases are often used to index the document or as features in further processing. This makes improvements in AKE accuracy particularly important. We hypothesized that filtering out marginally relevant sentences from a document would improve AKE accuracy. Our experiments confirmed this hypothesis. Elimination of as little as 10% of the document sentences lead to a 2% improvement in AKE precision and recall. AKE is built over MAUI toolkit that follows a supervised learning approach. We trained and tested our AKE method on a gold standard made of 8 BN programs containing 110 manually annotated news stories. The experiments were conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio news/programs, running daily, and monitoring 12 TV and 4 radio channels.

Keywords

Keyphrase extraction Speech summarization Speech browsing Broadcast News speech recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Luís Marujo
    • 1
    • 2
    • 4
  • Ricardo Ribeiro
    • 2
    • 3
  • David Martins de Matos
    • 2
    • 4
  • João P. Neto
    • 2
    • 4
  • Anatole Gershman
    • 1
  • Jaime Carbonell
    • 1
  1. 1.LTI/CMUUSA
  2. 2.L2F - INESC ID LisboaPortugal
  3. 3.Instituto Universitário de Lisboa (ISCTE-IUL)Portugal
  4. 4.Instituto Superior TécnicoUniversidade Técnica de LisboaPortugal

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