期刊
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 33, 期 3, 页码 1867-1879出版社
IOS PRESS
DOI: 10.3233/JIFS-161613
关键词
Multi-document summarization; sentence clustering; cluster-based ranking; non-negative matrix tri-factorization; manifold ranking
资金
- China Postdoctoral Science Foundation [2017M613205]
- Basic Research Project - Shenzhen Science and Technology Innovation Committee [201703063000511]
Multi-document summarization aims to produce a concise summary that contains salient information from a set of source documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence cluster using Nonnegative Matrix Tri-Factorization (NMTF). The proposed framework employs NMTF to cluster sentences using inter-type relationships among documents, sentences and terms, and incorporate the intra-type information through manifold regularization. The most informative sentences are selected from each sentence cluster to form the summary. When evaluated on the DUC2004 and TAC2008 datasets, the performance of the proposed framework is comparable with that of the top three systems.
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