Multi-document summarization using minimum distortion

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 354-363

  Ma, Tengfei ^a   Wan, Xiaojun ^a  

^a Peking University   (China)

Author keywords

Information theoretic summarization; J S divergence; Linear representation; Minimum distortion; Multi document summarization

Indexed keywords

INFORMATION-THEORETIC SUMMARIZATION; J-S DIVERGENCE; LINEAR REPRESENTATION; MINIMUM DISTORTION; MULTI-DOCUMENT SUMMARIZATION;

COMPUTATIONAL LINGUISTICS; DATA MINING; FACILITIES; INFORMATION THEORY;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 79951744930     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.106     Document Type: Conference Paper

References (33)

1. V. Arya, N. Garg, R. Khandekar, K. Munagala, V. Pandit, "Local search heuristic for k-median and facility location problems," Proceedings of the thirty-third annual ACM symposium on Theory of computing (STOC01), pp.21-29, July 2001, Hersonissos, Greece. (Pubitemid 32865714)
2. J. Carbonell, and J. Goldstein, "The Use of MMR, diversity-based reranking for reordering documents and producing summaries," in Proceding of SIGIR'98, pp. 335-336, New York, USA, 1998.
3. K. Chaudhuri, A. McGregor, "Finding Metric Structure in Information Theoretic Clustering," in Proceedings of COLT, 2008.
4. J. M. Conroy and D. P. Oárleary, "Text Summarization via Hidden Markov Models," in SIGIR2001, pp. 406-407, New York, NY, USA, 2001. ACM. (Pubitemid 32951104)
5. T. Cover and J. Thomas, "Elements of Information Theory", John Wiley & Sons, New York, USA, 1991.
6. D. Das, and A. Martins, "A Survey on Automatic Text Summarization," Literature Survey for the Language and Statistics II Course at CMU, 2007.
7. G. Erkan and D. Radev, "LexRank: Graph-based centrality as salience in text summarisation," Journal of Artificial Intelligence Research, vol. 22, pp. 457-479.
8. J. Goldstein, M. Kantrowitz, V. Mittal, and J. Carbonell, "Summarizing Text Documents: Sentence Selection and Evaluation Metrics," in SIGIR'99.
9. Y. Gong and X. Liu, "Generic text summarization using relevance measure and latent semantic analysis," in SIGIR'01, pp. 19-25, 2001. (Pubitemid 32951051)
10. S. Harabagiu, and F. Lacatusu, "Topic Themes for Multi-Document Summarization," in SIGIR'05.
11. P. Harremoes, N. Tishby, "The Information Bottleneck Revisited or How to Choose a Good Distortion Measure," in Proc. of the IEEE Int. Symp. on Information Theory (ISIT), 2007.
12. K. S. Jones, "Automatic summarising: The state of the art," Information Processing and Management, vol. 43, pp. 1449-1481, 2007.
13. J. Kupiec, J. Pedersen, and F. Chen, "A trainable document summarizer," in SIGIR, pp. 68-73, 1995.
14. L. Li, K. Zhou, G-R. Xue, H. Zha, and Y. Yu, "Enhancing Diversity, Coverage and Balance for Summarization through Structure Learning," in WWW 2009.
15. C-Y. Lin, G. Cao, J. Gao, and J-Y. Nie, "An Information-Theoretic Approach to Automatic Evaluation of Summaries," in NAACL06.
16. C.-Y. Lin and E. Hovy, "Automatic evaluation of summaries using n-gram co-occurrence statistics," in HLT-NAACL, 2003, pp. 71-78.
17. C. Long, M. Huang, X. Zhu, and M. Li, "Multi-Document Summarization by Information Distance," in ICDM2009.
18. C. Long, S. Chen, Y. Yu, F. Jin, L. Qin, M. Huang, X. Zhu, "Tsinghua University at the summarization track of TAC 2008," in TAC2008.
19. H. P. Luhn, "The Automatic Creation of Literature Abstracts," IBM Journal of Research and Development, vol. 2, no. 2, pp. 159-165, 1958.
20. I. Mani and E. Bloedorn, "Multi-document summarization by graph search and matching," In AAAI/IAAI, pp. 622-628, 1997.
21. D. Marcu, "From Discourse Structures to Text Summaries," in ACL'97/EACL'97 Workshop on Intelligent scalable Text Summarization, pages 82-88, 1997.
22. R. Mihalcea and P. Tarau, "A language independent algorithm for single and multiple document summarization," in Proceedings of IJCNLP2005.
23. D. R. Radev, H. Jing, M. Stys, and D. Tam, "Centroid-based summarization of multiple documents," Information Processing and Management, vol. 40 (2004), pp. 919-938.
24. G. Ravindra, N. Balakrishnan, K. R. Ranmakrishnan, "Multi-Document Automatic Text Summarization Using Entropy Estimates," in SOFSEM2004, pp. 289-300.
25. D. Shen, J-T. Sun, H. Li, Q. Yang, and Z. Cheng, "Document Summarization using Conditional Random Fields," in IJCAI07.
26. N. Tishby, F. C. Pereira, W. Bialek, "The information Bottleneck Method," in The 37th annual Allerton Conference on Communication, Control, and Computing, Sep 1999: pp. 368-377.
27. X. Wan and J. Yang, "Multi-document Summarization Using Cluster-based Link Analysis," in SIGIR08.
28. X. Wan and J. Yang, "Improved affinity graph based multi-document summarization," in Proceedings of HLT-NAACL2006.
29. K-F. Wong, M. Wu, and W. Li, "Extractive Summarization Using Supervised and Semi-supervised Learnsing," in Coling 2008, pp. 985-992.
30. S. Ye, T-S. Chua, M-Y. Kan, and L. Qiu, "Document concept lattice for text understanding and summarization," Information Processing and Management, vol. 43, pp. 1643-1662, 2007. (Pubitemid 47268115)
31. J-Y. Yeh, H-R. Ke, W-P. Yang, and I-H. Meng, "Text summarization using a trainable summarizer and latent semantic analysis," IPM, 41(1):75-95, 2005.
32. H. Zha, "Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering," in SIGIR'02.
33. C. Zhai and J. Lafferty, "A Study of Smoothing Methods for Language Models Applied to Information Retrieval," ACM Transactions on Information Systems, Vol 22, No. 2, April 2004, pp. 179-214.