A relevance feedback model for fractal summarization

  • Authors:

  • Fu Lee Wang;Christopher C. Yang

  • Affiliations:

  • Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China;Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China

  • Venue:
  • ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
  • Year:
  • 2004

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Abstract

As a result of the recent information explosion, there is an increasing demand for automatic summarization, and human abstractors often synthesize summaries that are based on sentences that have been extracted by machine. However, the quality of machine-generated summaries is not high. As a special application of information retrieval systems, the precision of automatic summarization can be improved by user relevance feedback, in which the human abstractor can direct the sentence extraction process and useful information can be retrieved efficiently. Automatic summarization with relevance feedback is a helpful tool to assist professional abstractors in generating summaries, and in this work we propose a relevance feedback model for fractal summarization. The results of the experiment show that relevance feedback effectively improves the performance of automatic fractal summarization.