標題: Text summarization using a trainable summarizer and latent semantic analysis
作者: Yeh, JY
Ke, HR
Yang, WP
Meng, IH
資訊工程學系
圖書館
Department of Computer Science
Library
關鍵字: text summarization;corpus-based approach;latent semantic analysis;text relationship map
公開日期: 1-一月-2005
摘要: This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + 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 LSA + 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 MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively. (C) 2004 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.ipm.2004.04.003
http://hdl.handle.net/11536/24779
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2004.04.003
期刊: INFORMATION PROCESSING & MANAGEMENT
Volume: 41
Issue: 1
起始頁: 75
結束頁: 95
顯示於類別:會議論文


文件中的檔案:

  1. 000224486000006.pdf