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  • 标题:Text Summarization Model based on Maximum Coverage Problem and its Variant
  • 本地全文:下载
  • 作者:Hiroya Takamura ; Manabu Okumura
  • 期刊名称:人工知能学会論文誌
  • 印刷版ISSN:1346-0714
  • 电子版ISSN:1346-8030
  • 出版年度:2008
  • 卷号:23
  • 期号:6
  • 页码:505-513
  • DOI:10.1527/tjsai.23.505
  • 出版社:The Japanese Society for Artificial Intelligence
  • 摘要:We discuss text summarization in terms of maximum coverage problem and its variant. To solve the optimization problem, we applied some decoding algorithms including the ones never used in this summarization formulation, such as a greedy algorithm with performance guarantee, a randomized algorithm, and a branch-and-bound method. We conduct comparative experiments. On the basis of the experimental results, we also augment the summarization model so that it takes into account the relevance to the document cluster. Through experiments, we showed that the augmented model is at least comparable to the best-performing method of DUC'04.
  • 关键词:text summarization ; decoding algorithm ; approximate algorithm ; integer programming ; maximum coverage problem
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