Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10355256 | Information Processing & Management | 2005 | 21 Pages |
Abstract
This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSAÂ +Â T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSAÂ +Â T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBAÂ +Â GA, 44% and 40% for LSAÂ +Â T.R.M. in single-document and corpus level were achieved respectively.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Jen-Yuan Yeh, Hao-Ren Ke, Wei-Pang Yang, I-Heng Meng,