Text summarization using a trainable summarizer and latent semantic analysis |
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Institution: | 1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China;2. CETC Big Data Research Institute Co., Ltd., Guiyang 550022, China;1. Department of Computer and Communications Technologies, University of Extremadura, Campus Universitario s/n, Caceres 10003, Spain;2. Department of Mathematics, University of Extremadura, Campus Universitario s/n, Caceres 10003, Spain |
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Abstract: | 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. |
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