Topic
Multi-document summarization
About: Multi-document summarization is a research topic. Over the lifetime, 2270 publications have been published within this topic receiving 71850 citations.
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TL;DR: A strategy for Chinese review multi-document summarization based on opinion extraction and opinion similarity is proposed, where sentences which contain more important and less redundant information are optimized as summary.
2 citations
Proceedings Article•
11 Oct 2007TL;DR: Schema summarization uses abstract elements and links to summarize a complex schema and allows users with limited knowledge of the underlying database structure to effectively issue queries to the CDR for clinical and translational research.
Abstract: The University of Michigan Clinical Data Repository (CDR) integrates over 25 data sources, and as a result has a schema that is too complex to be directly queried by clinical researchers. Schema summarization uses abstract elements and links to summarize a complex schema and allows users with limited knowledge of the underlying database structure to effectively issue queries to the CDR for clinical and translational research.
2 citations
31 Dec 2012
TL;DR: A new unsupervised sentence compression method that relies on the semantic roles of sentences' parts and is applied in the context of multi-document summarization.
Abstract: In this paper, a new unsupervised sentence compression method is proposed. Sentences are tagged with Part Of Speech tags and semantic role labels. The proposed method relies on the semantic roles of sentences' parts. Moreover, in the process of compression, other sentences in the context are taken into account. The approach is applied in the context of multi-document summarization. Experiments showed better results than other state of the art approaches.
2 citations
07 Aug 2006
TL;DR: A strategy for Chinese multidocument summarization based on clustering and sentence extraction based on stability method and a global searching method for sentence selection from the clusters is proposed.
Abstract: Multi-document summarization has become a key technology in natural language processing. This paper proposes a strategy for Chinese multidocument summarization based on clustering and sentence extraction. As for clustering, we propose two heuristics to automatically detect the proper number of clusters: the first one makes full use of the summary length fixed by the user; the second is a stability method, which has been applied to other unsupervised learning problems. We also discuss a global searching method for sentence selection from the clusters. To evaluate our summarization strategy, an extrinsic evaluation method based on classification task is adopted. Experimental results on news document set show that the new strategy can significantly enhance the performance of Chinese multi-document summarization.
2 citations