Centroid-based summarization of multiple documents

Information Processing and Management

Volumn 40, Issue 6, 2004, Pages 919-938

  Radev, Dragomir R ^a   Jing, Hongyan ^b   Styś, Małgorzata ^b   Tam, Daniel ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Centroid based summarization; Cluster based relative utility; Cross sentence informational subsumption; Multi document summarization

Indexed keywords

ALGORITHMS; INFORMATION ANALYSIS; PROBLEM SOLVING; PROGRAM DOCUMENTATION; QUERY LANGUAGES; STATISTICAL METHODS;

CENTROID-BASED SUMMARIZATION; CLUSTER-BASED RELATIVE UTILITY; CROSS-SENTENCE INFORMATIONAL SUBSUMPTION; MULTI-DOCUMENT SUMMARIZATION;

DATA PROCESSING;

EID: 4243064897     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2003.10.006     Document Type: Article

References (13)

1. Allan, J., Carbonell, J., Doddington, G., Yamron, J., & Yang, Y. (1998a). In Proceedings of the broadcast news understanding and transcription workshop. In Topic detection and tracking pilot study: final report.
2. Allan, J., Papka, R., & Lavrenko, V. (1998b). On-line new event detection and tracking. In Proceedings of the 21st annual international ACM 57-GIR conference on research and development in information retrieval (pp. 37-45). Melbourne, Australia.
3. Aone, C., Okurowski, M. E., Gorlinsky, J., & Larsen, B. (1997). A scalable summarization system using robust NLP. In Proceedings of the workshop on intelligent scalable text summarization at the 35th meeting of the association for computational linguistics, and the 8th conference of the European chapter of the assocation for computational linguistics (pp. 66-73).
4. Carbonell, J. G., & Goldstein, J. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. In A. Mof-fat, & J. Zobel (Eds.), Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval (pp. 335-336). Melbourne, Australia.
5. Goldstein, J., Kantrowitz, M., Mittal, V. O., & Carbonell, J. G. (1999). Summarizing text documents: sentence selection and evaluation metrics. In Research and development in information retrieval (pp. 121-128). Berkeley, California.
6. Jing, H., McKeown, K. R., Barzilay, R., & Elhadad, M. (1998). Summarization evaluation methods: experiments and analysis. In E. Hovy, D. R. Radev (Eds.), Intelligent Text Summarization. Papers from the 1998 AAAI Spring Symposium. Technical Report SS-98-06 (pp. 60-68). The AAAI Press, Stanford, California, USA.
7. Mani I., Bloedorn E. Summarizing similarities and differences among related documents. Information Retrieval. 1(1):2000.
8. Mani I., Maybury M.T. Advances in Automatic Text Summarization. 1999;MIT Press, Cambridge, MA.
9. McKeown, K. R., Klavans, J., Hatzivassiloglou, V., Barzilay, R., & Eskin, E. (1999). Towards multidocument summarization by reformulation: progress and prospects. In Proceeding of the 16th national conference of the American association for artificial intelligence (AAAI-1999) (pp. 453-460).
10. Radev, D. R., Hatzivassiloglou, V., & McKeown, K. R. (1999). A description of the CIDR system as used for TDT-2. In DARPA broadcast news workshop. Herndon, Virginia.
11. Radev D.R., McKeown K.R. Generating natural language summaries from multiple on-line sources. Computational Linguistics. 4:1998;469-500.
12. Radev, D. R., & Tam, D. (2003). Single-document and multi-document summary evaluation via relative utility. In Poster Session, Proceedings of the ACM CIKM conference. New Orleans, LA.
13. Radev, D. R., Teufel, S., Saggion, H., Lam, W., Blitzer, J. H. Q., Çelebi, A., Liu, D., & Drabek, E. (2003). Evaluation challenges in large-scale multi-document summarization: the mead project. In Proceedings of ACL 2003. Sapporo, Japan.