Inclusion-based and exclusion-based approaches in graph-based multiple news summarization

  • Authors:

  • Nongnuch Ketui;Thanaruk Theeramunkong

  • Affiliations:

  • School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University;School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University

  • Venue:
  • KICSS'10 Proceedings of the 5th international conference on Knowledge, information, and creativity support systems
  • Year:
  • 2010

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Abstract

As combination of information extraction and relation analysis, constructing a comprehensive summary from multiple documents is a challenging task. Towards summarization of multiple news articles related to a specific event, an ideal summary should include only important common descriptions of these articles, together with some dominant differences among them. This paper presents a graph-based summarization method which is composed of text preprocessing, text-portion segmentation, weight assignment of text portions, and relation analysis among text portions, text-portion graph construction, and significant portion selection. In the process of portion selection, this paper proposes two alternative methods; inclusion-based and exclusion-based approach. To evaluate these approaches, a set of experiments are conducted on fifteen sets of Thai political news articles. Measured with ROUGE-N, the result shows that the inclusion-based approach outperforms the exclusion-based one with approximately 2% performance gap (80.59 to 78.21%).