论文信息 - Genetic algorithm based multi-document summarization

Genetic algorithm based multi-document summarization

The multi-document summarizer using genetic algorithm-based sentence extraction (SBGA) regards summarization process as an optimization problem where the optimal summary is chosen among a set of summaries formed by the conjunction of the original articles sentences. To solve the NP hard optimization problem, SBGA adopts genetic algorithm, which can choose the optimal summary on global aspect. To improve the accuracy of term frequency, SBGA employs a novel method TFS, which takes word sense into account while calculating term frequency. The experiments on DUC04 data show that our strategy is effective and the ROUGE-1 score is only 0.55% lower than the best participant in DUC04.

Donghong Ji | Yanxiang He | Hua Yang | Dexi Liu

[1] Dragomir R. Radev,et al. Centroid-based summarization of multiple documents , 2004, Inf. Process. Manag..

[2] Eduard H. Hovy,et al. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics , 2003, NAACL.

[3] Regina Barzilay,et al. Information Fusion in the Context of Multi-Document Summarization , 1999, ACL.

[4] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[5] Daniel Marcu,et al. Summarization beyond sentence extraction: A probabilistic approach to sentence compression , 2002, Artif. Intell..

[6] Dragomir R. Radev,et al. Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies , 2000, ArXiv.

[7] Manuel de Buenaga,et al. Multidocument summarization: An added value to clustering in interactive retrieval , 2004 .