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Summarizing Similarities and Differences Among Related Documents
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NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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This paper proposes a unified extractive approach based on affinity graph to both generic and topic-focused multi-document summarizations. By using an asymmetric similarity measure, the relationships between sentences are reflected in a directed affinity graph for generic summarization. For topic-focused summarization, the topic information is incorporated into the affinity graph using a topic-sensitive affinity measure. Based on the affinity graph, the information richness of sentences is computed by the graph-ranking algorithm on differentiated intra-document links and inter-document links between sentences. Lastly, the greedy algorithm is employed to impose diversity penalty on sentences and the sentences with both high information richness and high information novelty are chosen into the summary. Experimental results on the tasks of DUC 2002-2005 demonstrate the excellent performances of the proposed approaches to both generic and topic-focused multi-document summarization tasks.