A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
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WWW7 Proceedings of the seventh international conference on World Wide Web 7
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to the special issue on summarization
Computational Linguistics - Summarization
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ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Automatic evaluation of summaries using N-gram co-occurrence statistics
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Query-sensitive similarity measures for information retrieval
Knowledge and Information Systems
Neural Computation
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HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Using Cross-Document Random Walks for Topic-Focused Multi-Document
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Using lexical chains for keyword extraction
Information Processing and Management: an International Journal
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Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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IBM Journal of Research and Development
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ACM Transactions on Intelligent Systems and Technology (TIST)
Search and analysis of bankruptcy cause by classification network
MEDI'11 Proceedings of the First international conference on Model and data engineering
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Graph-based models have been extensively explored in document summarization in recent years. Compared with traditional feature-based models, graph-based models incorporate interrelated information into the ranking process. Thus, potentially they can do a better job in retrieving the important contents from documents. In this paper, we investigate the problem of how to measure sentence similarity which is a crucial issue in graph-based summarization models but in our belief has not been well defined in the past. We propose a supervised learning approach that brings together multiple similarity measures and makes use of human-generated summaries to guide the combination process. Therefore, it can be expected to provide more accurate estimation than a single cosine similarity measure. Experiments conducted on the DUC2005 and DUC2006 data sets show that the proposed learning approach is successful in measuring similarity. Its competitiveness and adaptability are also demonstrated.