The Journal of Machine Learning Research
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Using random walks for question-focused sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Enhancing diversity, coverage and balance for summarization through structure learning
Proceedings of the 18th international conference on World wide web
Manifold-ranking based topic-focused multi-document summarization
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploiting novelty, coverage and balance for topic-focused multi-document summarization
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A context-sensitive manifold ranking approach to query-focused multi-document summarization
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
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Query-oriented relevance, information richness and novelty are important requirements in query-focused summarization, which, to a considerable extent, determine the summary quality. Previous work either rarely took into account all above demands simultaneously or dealt with part of them in the dynamic process of choosing sentences to generate a summary. In this paper, we propose a novel approach that integrates all these requirements skillfully by treating them as sentence features, making that the finally generated summary could fully reflect the combinational effect of these properties. Experimental results on the DUC2005 and DUC2006 datasets demonstrate the effectiveness of our approach.