A novel relational learning-to-rank approach for topic-focused multi-document summarization

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2013, Pages 927-936

  Zhu, Yadong ^a   Lan, Yanyan ^a   Guo, Jiafeng ^a   Du, Pan ^a   Cheng, Xueqi ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84894655835     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2013.38     Document Type: Conference Paper

References (30)

1. P. Du, J. Guo, and X. Cheng, "Supervised lazy random walk for topic-focused multi-document summarization," in Proceedings of the 11th ICDM, ser. ICDM '11, 2011, pp. 1026-1031.
2. X.-Q. Cheng, P. Du, J. Guo, X. Zhu, and Y. Chen, "Ranking on data manifold with sink points," IEEE Trans. on Knowl. and Data Eng., vol. 25, no. 1, pp. 177-191, Jan. 2013.
3. T. Ma and X. Wan, "Multi-document summarization using minimum distortion," in Proceedings of the 10th ICDM, ser. ICDM '10, 2010, pp. 354-363.
4. J. Carbonell and J. Goldstein, "The use of mmr, diversitybased reranking for reordering documents and producing summaries," in Proceedings of the 21st ACM SIGIR, 1998, pp. 335-336.
5. S. T. Davis, J. M. Conroy, and J. D. Schlesinger, "Occams-an optimal combinatorial covering algorithm for multidocument summarization," 2012 IEEE 12th ICDM Workshops, vol. 0, pp. 454-463, 2012.
6. Q. Mei, J. Guo, and D. Radev, "Divrank: the interplay of prestige and diversity in information networks," in Proceedings of the 16th ACM SIGKDD, ser. KDD '10, 2010, pp. 1009-1018.
7. T.-Y. Liu, Learning to Rank for Information Retrieval. Springer, 2011.
8. J. I. Marden, Analyzing and Modeling Rank Data. Chapman and Hall, 1995.
9. X. Li, Y.-D. Shen, L. Du, and C.-Y. Xiong, "Exploiting novelty, coverage and balance for topic-focused multi-document summarization," in Proceedings of the 19th ACM CIKM, ser. CIKM '10, 2010, pp. 1765-1768.
10. S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, "Indexing by latent semantic analysis," JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE, vol. 41, no. 6, pp. 391-407, 1990.
11. G. Erkan and D. R. Radev, "Lexrank: graph-based lexical centrality as salience in text summarization," J. Artif. Int. Res., vol. 22, no. 1, pp. 457-479, Dec. 2004.
12. S. Brin and L. Page, "The anatomy of a large-scale hypertextual web search engine," in Proceedings of the 7th WWW, ser. WWW7, 1998, pp. 107-117.
13. J. Otterbacher, G. Erkan, and D. R. 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, ser. HLT '05, 2005, pp. 915-922.
14. H. Zha, "Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering," in Proceedings of the 25th ACM SIGIR, ser. SIGIR '02, 2002, pp. 113-120.
15. J. M. Kleinberg, "Authoritative sources in a hyperlinked environment," J. ACM, vol. 46, no. 5, pp. 604-632, Sep. 1999.
16. X. Wan, J. Yang, and J. Xiao, "Manifold-ranking based topicfocused multi-document summarization," in Proceedings of the 20th IJCAI, ser. IJCAI'07, 2007, pp. 2903-2908.
17. X. Zhu, A. B. Goldberg, J. Van, and G. D. Andrzejewski, "Improving diversity in ranking using absorbing random walks," in Physics Laboratory C University of Washington, 2007, pp. 97-104.
18. V. N. Vapnik, The nature of statistical learning theory. New York, NY, USA: Springer-Verlag New York, Inc., 1995.
19. C. J. V. Rijsbergen, Information Retrieval, 2nd ed. Newton, MA, USA: Butterworth-Heinemann, 1979.
20. D. Metzler and T. Kanungo, "Machine learned sentence selection strategies for query-biased summarization," in Proceedings of SIGIR Learning to Rank Workshop, 2008.
21. Y. Ouyang, W. Li, S. Li, and Q. Lu, "Applying regression models to query-focused multi-document summarization," Inf. Process. Manage., vol. 47, no. 2, pp. 227-237, Mar. 2011.
22. J. M. Conroy and D. P. O'leary, "Text summarization via hidden markov models," in Proceedings of the 24th ACM SIGIR, ser. SIGIR '01, 2001, pp. 406-407.
23. D. Shen, J.-T. Sun, H. Li, Q. Yang, and Z. Chen, "Document summarization using conditional random fields," in Proceedings of the 20th IJCAI, ser. IJCAI'07, 2007, pp. 2862-2867.
24. L. Li, K. Zhou, G.-R. Xue, H. Zha, and Y. Yu, "Enhancing diversity, coverage and balance for summarization through structure learning," in Proceedings of the 18th WWW, ser. WWW '09, 2009, pp. 71-80.
25. T. Hofmann, "Probabilistic latent semantic indexing," in Proceedings of the 22nd ACM SIGIR, ser. SIGIR '99, 1999, pp. 50-57.
26. T. Qin, T.-Y. Liu, J. Xu, and H. Li, "Letor: A benchmark collection for research on learning to rank for information retrieval," Inf. Retr., pp. 346-374, 2010.
27. D. Metzler and W. B. Croft, "A markov random field model for term dependencies," in Proc. of the 28th ACM SIGIR, 2005, pp. 472-479.
28. C.-Y. Lin, "Rouge: A package for automatic evaluation of summaries," in Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, July 2004, pp. 74-81.
29. T. H. Haveliwala, "Topic-sensitive pagerank," in Proceedings of the 11th WWW, ser. WWW '02, 2002, pp. 517-526.
30. D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Scholkopf, "Ranking on data manifolds," in Advances in Neural Information Processing Systems 16. MIT Press, 2004.