A Fuzzy-Rough Hybrid Approach to Multi-document Extractive Summarization
To generate a multi-document extractive summary, the measurement of sentence relevance is of vital importance. Earlier work, exploring statistics of textual terms at the word (surface) level, faces the problem that the textual terms may be synonymous or ploysemous. This may lead to misrank sentence relevance and may cause redundant information presented in the generated summary. Furthermore, the relationships between concepts expressed by natural languages are inherently fuzzy, which invites the use of fuzzy set and rough set theory. In this paper, we investigate some sentence features from a conceptlevel space and apply a fuzzy-rough hybrid scheme to de.ne a sentence relevance measure. Our approach is applied to the DUC 2006 multi-document summarization tasks. The experimental results show our approach is promising and demonstrate the effectiveness of fuzzy set and rough set theory in the application of text summarization.
Hsun-Hui Huang Horng-Chang Yang Yau-Hwang kuo
Dept. of Computer Science and Information Engineering National Cheng Kung Univ. Tainan, Taiwan Dept. Dept. of Computer Science and Information Engineering National Taitung Univ. Taitung, Taiwan Dept. of Computer Science and Information Engineering National Cheng Kung Univ. Tainan, Taiwan
国际会议
2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)
沈阳
英文
1-6
2009-08-12（万方平台首次上网日期，不代表论文的发表时间）