Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization

ACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings

Volumn , Issue , 2008, Pages 283-290

  Wei, Furu ^a,b   Li, Wenjie ^a   Lu, Qin ^a   He, Yanxiang ^b  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b WUHAN UNIVERSITY   (China)

Author keywords

Mutual reinforcement chain; Query oriented summarization; Query sensitive similarity; Ranking algorithms

Indexed keywords

INFORMATION RETRIEVAL; INFORMATION SERVICES; RAYLEIGH SCATTERING; RESEARCH AND DEVELOPMENT MANAGEMENT;

DOCUMENT SUMMARIZATIONS; KEY PHRASES; MATHEMATICAL MODELING; MULTI DOCUMENTS; MUTUAL REINFORCEMENT CHAIN; MUTUAL REINFORCEMENTS; ONE STEPS; PROMISING APPROACHES; QUERY-ORIENTED SUMMARIZATION; QUERY-SENSITIVE SIMILARITY; RANKING ALGORITHMS; SENTENCE EXTRACTIONS;

REINFORCEMENT;

EID: 57349156610     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390334.1390384     Document Type: Conference Paper

References (23)

1. th WWW, pp 757-766.
2. Brin, S. and Page, L. 1998. The Anatomy of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1-7): 107-117.
3. Cilibrasi, R. L. and Vitanyi, P. M. B. 2007. The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 3, pp 370-383.
4. DUC: http://www-nlpir.nist.gov/projects/duc/pubs.htoil.
5. Erkan, G. and Radev, D. R. 2004. LexRank: Graph-based Centrality as Salience in Text Summarization, Journal of Artificial Intelligence Research 22:457-479.
6. Haveliwala, T. H. 2003. Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search. IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 4, pp 784-796.
7. Langville, A. N. and Meyer, C. D. 2004. Deeper Inside PageRank. Journal of Internet Mathematics, 1(3): 335-380.
8. th COLING, pp 495-501.
9. Lin, C. Y. and Hovy, E. 2003. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics, in Proceedings of HLT-NAACL, PP71-78.
10. Mani, I. and Maybury, M. T.(Eds.). 1999. Advances in Automatic Summarization. The MIT Press.
11. Mihalcea, R. 2004. Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization. In Proceedings of ACL 2004, Article No. 20.
12. Mihalcea, R. 2005. Language Independent Extractive Summarization. In Proceedings of ACL 2005, pp 49-52.
13. Jones, K. S. 2007. Automatic Summarising: The State of the art. Information Processing and Management 43: 1449-1481.
14. th Annual ACM-SIAM Symposium on Discrete Algorithms. Pp 668-677.
15. Otterbacher, J., Erkan, G., and Radev, D. R. 2005. Using Random. Walks for Question-focused Sentence Retrieval. In Proceedings of HLT/EMNLP, pp 915-922.
16. th CIKM, pp 79-86.
17. Over, P., Dang, H., and Harman, D. 2007. DUC in Context, Information Processing and Management, 43(6): 1506-1520.
18. Radev, D. R., Jing, H. Y., Stys, M., and Tam, D. 2004. Centroid-based Summarization of Multiple Documents. Information Processing and Management, 40: 919-938.
19. th WWW, pp 377-386.
20. Tombros, A. and Rijsbergen, C. J. v. 2004. Query-Sensitive Similarity Measures for Information Retrieval. Knowledge and Information Systems (2004) 6: 617-642.
21. Wan, X. Y., Yang, J. W., and Xiao, J. G. 2007. Towards Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction. In Proceedings of ACL.
22. th ACM SIGIR, ppl 13-120.
23. Zhou, Z. H. and Dai, H. B. 2006. Query-Sensitive Similarity Measure for Content-Based Image Retrieval. In Proceedings of ICDM, pp1211-1215.