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
Training a selection function for extraction
Proceedings of the eighth international conference on Information and knowledge management
Retrieval and novelty detection at the sentence level
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Text summarization using a trainable summarizer and latent semantic analysis
Information Processing and Management: an International Journal - Special issue: An Asian digital libraries perspective
Bayesian query-focused summarization
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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Query-focused multidocument summarization is to synthesize from a set of topic-related documents a brief, well-organized, fluent summary for the purpose of answering an information need that cannot be met by just stating a name, date, quantity, etc. In this paper, the task is essentially treated as a sentence retrieval task. We propose a hybrid relevance analysis to evaluate the relevance of a sentence to the query. This is achieved by combining similarities computed from the vector space model and latent semantic analysis. Surface features are also examined to discern the impact of low-level features for query-focused multidocument summarization. In addition, a modified Maximal Marginal Relevance is proposed to reduce redundancy by taking into account shallow feature salience. The experimental results show the proposed method obtained competitive results when evaluated with the DUC 2005 corpus.