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Identification of Event and Topic for Multi-document Summarization

  • Fumiyo FukumotoEmail author
  • Yoshimi Suzuki
  • Atsuhiro Takasu
  • Suguru Matsuyoshi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9561)

Abstract

This paper focuses on continuous news documents and presents a method for extractive multi-document summarization. Our hypothesis about salient, key sentences in news documents is that they include words related to the target event and topic of a document. Here, an event and a topic are the same as Topic Detection and Tracking (TDT) project: an event is something that occurs at a specific place and time along with all necessary preconditions and unavoidable consequences, and a topic is defined to be “a seminal event or activity along with all directly related events and activities.” The difficulty for finding topics is that they have various word distributions. In addition to the TF-IDF term weighting method to extract event words, we identified topics by using two models, i.e., Moving Average Convergence Divergence (MACD) for words with high frequencies, and Latent Dirichlet Allocation (LDA) for low frequency words. The method was tested on two datasets, NTCIR-3 Japanese news documents and DUC data, and the results showed the effectiveness of the method.

Keywords

Latent Dirichlet Allocation Moving Average Convergence/Divergence Multi-document summarization 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fumiyo Fukumoto
    • 1
    Email author
  • Yoshimi Suzuki
    • 1
  • Atsuhiro Takasu
    • 2
  • Suguru Matsuyoshi
    • 3
  1. 1.Graduate Faculty of Interdisciplinary ResearchUniversity of YamanashiKofuJapan
  2. 2.National Institue of InformaticsTokyoJapan
  3. 3.Interdisciplinary Graduate School of Medicine and EngineeringUniversity of YamanashiKofuJapan

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