Cross-document summarization by concept classification
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering based on cluster validation
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Information fusion in the context of multi-document summarization
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
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Syntactic simplification for improving content selection in multi-document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
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In this paper we propose a clustering-based hybrid approach for multi-document summarization which integrates sentence clustering, local recommendation and global search. For sentence clustering, we adopt a stability-based method which can determine the optimal cluster number automatically. We weight sentences with terms they contain for local sentence recommendation of each cluster. For global selection, we propose a global criterion to evaluate overall performance of a summary. Thus the sentences in the final summary are determined by not only the configuration of individual clusters but also the overall performance. This approach successfully gets top-level performance running on corpus of DUC04.