期刊
INFORMATION PROCESSING & MANAGEMENT
卷 45, 期 1, 页码 20-34出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2008.06.002
关键词
Generic summarization; NMF; LSA; Semantic feature; Semantic variable
资金
- Inha University
In existing unsupervised methods, Latent Semantic Analysis (LSA) is used for sentence selection. However, the obtained results are less meaningful, because singular vectors are used as the bases for sentence selection from given documents, and singular vector components can have negative values. We propose a new unsupervised method using Non-negative Matrix Factorization (NMF) to select sentences for automatic generic document summarization. The proposed method uses non-negative constraints, which are more similar to the human cognition process. As a result, the method selects more meaningful sentences for generic document summarization than those selected using LSA. (C) 2008 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据