Learning to rank for query-focused multi-document summarization

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 626-634

  Shen, Chao ^a   Li, Tao ^a  

^a FLORIDA INTERNATIONAL UNIVERSITY   (United States)

Author keywords

Learning to rank; Query based multi document summarization

Indexed keywords

COST-SENSITIVE; FEATURE WEIGHT; LEARNING TO RANK; MULTI-DOCUMENT SUMMARIZATION; OBJECTIVE FUNCTIONS; RANKING SVM; SENTENCE EXTRACTION; SUPERVISED LEARNING METHODS; TRAINING CORPUS; TRAINING DATA;

DATA MINING;

EID: 84863118985     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.91     Document Type: Conference Paper

References (27)

1. K. Knight and D. Marcu, "Summarization beyond sentence extraction: A probabilistic approach to sentence compression," Artificial Intelligence, vol. 139, no. 1, pp. 91-107, 2002.
2. V. Nastase, "Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation," in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2008, pp. 763-772.
3. C. Long, M. Huang, X. Zhu, and M. Li, "Multi-document summarization by information distance," in Proceedings of the 9th IEEE International Conference on Data Mining. IEEE, 2009, pp. 866-871.
4. D. Wang, T. Li, S. Zhu, and C. Ding, "Multi-document summarization via sentence-level semantic analysis and sym- metric matrix factorization," in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2008, pp. 307-314.
5. C. Shen and T. Li, "Multi-document summarization via the minimum dominating set," in Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, 2010, pp. 984-992.
6. J. Otterbacher, G. Erkan, and D. Radev, "Using random walks for question-focused sentence retrieval," in Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2005, pp. 915-922.
7. X. Wan, J. Yang, and J. Xiao, "Manifold-ranking based topicfocused multi-document summarization," in Proceedings of the 20th international joint conference on Artifical intelligence. Morgan Kaufmann Publishers Inc., 2007, pp. 2903- 2908.
8. D. Jurafsky and J. Martin, Speech and language processing. Prentice Hall New York, 2008.
9. Y. Ouyang, S. Li, and W. Li, "Developing learning strategies for topic-based summarization," in Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. ACM, 2007, pp. 79-86.
10. T. Joachims, "Optimizing search engines using clickthrough data," in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002, pp. 133-142.
11. J. Kupiec, J. Pedersen, and F. Chen, "A trainable document summarizer," in Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1995, pp. 68-73.
12. T. Hirao, H. Isozaki, E. Maeda, and Y. Matsumoto, "Extracting important sentences with support vector machines," in Proceedings of the 19th international conference on Computational linguistics. Association for Computational Linguistics, 2002, pp. 1-7.
13. L. Zhou and E. Hovy, "A web-trained extraction summarization system," in Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Association for Computational Linguistics, 2003, pp. 205-211.
14. D. Shen, J. Sun, H. Li, Q. Yang, and Z. Chen, "Document summarization using conditional random fields," in Proceedings of the 20th international joint conference on Artifical intelligence, vol. 7, 2007, pp. 2862-2867.
15. L. Zhao, X. Huang, and L. Wu, "Fudan University at DUC 2005," in Proceedings of DUC, vol. 2005, 2005.
16. Z. Cao, T. Qin, T. Liu, M. Tsai, and H. Li, "Learning to rank: from pairwise approach to listwise approach," in Proceedings of the 24th international conference on Machine learning. ACM, 2007, pp. 129-136.
17. M. Taylor, J. Guiver, S. Robertson, and T. Minka, "Softrank: optimizing non-smooth rank metrics," in Proceedings of the international conference on Web search and web data mining. ACM, 2008, pp. 77-86.
18. T. Liu, "Learning to rank for information retrieval," Foundations and Trends in Information Retrieval, vol. 3, no. 3, pp. 225-331, 2009.
19. Y. Freund, R. Iyer, R. Schapire, and Y. Singer, "An efficient boosting algorithm for combining preferences," The Journal of Machine Learning Research, vol. 4, pp. 933-969, 2003.
20. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, "Learning to rank using gradient descent," in Proceedings of the 22nd international conference on Machine learning. ACM, 2005, pp. 89-96.
21. J. Xu, Y. Cao, H. Li, and Y. Huang, "Cost-sensitive learning of SVM for ranking," Machine Learning: European Conference on Machine Learning, pp. 833-840, 2006.
22. Y. Cao, J. Xu, T. Liu, H. Li, Y. Huang, and H. Hon, "Adapting ranking SVM to document retrieval," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2006, pp. 186-193.
23. R. Herbrich, T. Graepel, and K. Obermayer, "Large margin rank boundaries for ordinal regression," Advances in Neural Information Processing Systems, pp. 115-132, 1999.
24. C. Lin and E. Hovy, "Automatic evaluation of summaries using n-gram co-occurrence statistics," in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology. Association for Computational Linguistics, 2003, pp. 71-78.
25. T. Joachims, "Svm-rank: Support vector machine for ranking," http://www.cs.cornell.edu/people/tj/svm-light/svm-rank.html, 2009.
26. A. Nenkova, L. Vanderwende, and K. McKeown, "A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization," in Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2006, pp. 573-580.
27. P. Pingali, K. Rahul, and V. Varma, "IIIT Hyderabad at DUC 2007," in Proceedings of DUC 2007, 2007.