Experiments in multidocument summarization

  • Authors:

  • Barry Schiffman;Ani Nenkova;Kathleen McKeown

  • Affiliations:

  • Columbia University, New York, NY;Columbia University, New York, NY;Columbia University, New York, NY

  • Venue:
  • HLT '02 Proceedings of the second international conference on Human Language Technology Research
  • Year:
  • 2002

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Abstract

This paper describes a multidocument summarizer built upon research into the detection of new information. The summarizer uses several new strategies to select interesting and informative sentences, including an innovative measure of importance derived from the analysis of a large corpus. The system also computes concept frequencies rather than word frequencies as an additional measure of importance. It merges these strategies with a number of familiar summarization heuristics to rank sentences. The initial version of the summarizer performed successfully in the evaluation reported at the Document Understanding Conference last year, although the system addressed only the content of the summary and not the presentation. We also discuss here the procedures we are developing to improve the presentation and readability of the summaries.