2007 ManifoldRankingTopicFocMultiDocSumm

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Subject Headings: Topic-focused Multi-Document Summarization Algorithm.

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Abstract

Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile. This paper presents a novel extractive approach based on manifold-ranking of sentences to this summarization task. The manifold-ranking process can naturally make full use of both the relationships among all the sentences in the documents and the relationships between the given topic and the sentences. The ranking score is obtained for each sentence in the manifold-ranking process to denote the biased information richness of the sentence. Then the greedy algorithm is employed to impose diversity penalty on each sentence. The summary is produced by choosing the sentences with both high biased information richness and high information novelty. Experiments on DUC2003 and DUC2005 are performed and the ROUGE evaluation results show that the proposed approach can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches.


References

  • 1. J. M. Conroy and J. D. Schlesinger. (2005). CLASSY query-based multidocument summarization. In: Proceedings of DUC'2005.
  • 2. G. Erkan and Dragomir Radev. LexPageRank: prestige in multi-document text summarization. In: Proceedings of EMNLP'2004.
  • 3. A. Farzindar, F. Rozon and G. Lapalme. (2005). CATS a topic-oriented multidocument summarization system at DUC 2005. In: Proceedings of the 2005 Document Understanding Workshop (DUC2005).
  • 4. J. Ge, X. Huang and L. Wu. Approaches to event-focused summarization based on named entities and query words. In: Proceedings of the 2003 Document Understanding Workshop (DUC2003).
  • 5. Jade Goldstein, Mark Kantrowitz, Vibhu Mittal, Jaime Carbonell, Summarizing text documents: sentence selection and evaluation metrics, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.121-128, August 15-19, 1999, Berkeley, California, United States doi:10.1145/312624.312665.
  • 6. Sanda Harabagiu, Finley Lacatusu, Topic themes for multi-document summarization, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil doi:10.1145/1076034.1076071.
  • 7. Hilda Hardy, Nobuyuki Shimizu, Tomek Strzalkowski, Liu Ting, Xinyang Zhang, G. Bowden Wise, Cross-document summarization by concept classification, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland doi:10.1145/564376.564399
  • 8. Eduard Hovy, C.-Y. Lin and L. Zhou. (2005). A BE-based multi-document summarizer with query interpretation. In: Proceedings of DUC2005.
  • 9. Chin-Yew Lin, Eduard Hovy, From single to multi-document summarization: a prototype system and its evaluation, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania doi:10.3115/1073083.1073160
  • 10. Chin-Yew Lin, Eduard Hovy, Automatic evaluation of summaries using N-gram co-occurrence statistics, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.71-78, May 27-June 01, 2003, Edmonton, Canada doi:10.3115/1073445.1073465
  • 11. Inderjeet Mani, Eric Bloedorn, Summarizing Similarities and Differences Among Related Documents, Information Retrieval, v.1 n.1-2, p.35-67, 1999 doi:10.1023/A:1009930203452
  • 12. R. Mihalcea and P. Tarau. A language independent algorithm for single and multiple document summarization. In: Proceedings of IJCNLP'2005.
  • 13. Dragomir Radev, Hongyan Jing, Malgorzata Stys, Daniel Tam, Centroid-based summarization of multiple documents, Information Processing and Management: an International Journal, v.40 n.6, p.919-938, November 2004 doi:10.1016/j.ipm.2003.10.006
  • 14. Horacio Saggion, Kalina Bontcheva, Hamish Cunningham, Robust generic and query-based summarisation, Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics, April 12-17, 2003, Budapest, Hungary doi:10.3115/1067737.1067793.
  • 15. Benyu Zhang, Hua Li, Yi Liu, Lei Ji, Wensi Xi, Weiguo Fan, Zheng Chen, Wei-Ying Ma, Improving web search results using affinity graph, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil doi:10.1145/1076034.1076120
  • 16. D. Zhou, O. Bousquet, T. N. Lal, J. Weston and B. Schölkopf. Learning with local and global consistency. In: Proceedings of NIPS'2003.
  • 17. D. Zhou, J. Weston, A. Gretton, O. Bousquet and B. Schölkopf. Ranking on data manifolds. In: Proceedings of NIPS'2003.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2007 ManifoldRankingTopicFocMultiDocSummXiaojun Wan
Jianwu Yang
Jianguo Xiao		Manifold-Ranking Based Topic-Focused Multi-Document SummarizationIJCAI 2007http://www.ijcai.org/papers07/Papers/IJCAI07-467.pdf2007
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