Single-Document Summarization Using Latent Semantic Analysis

Oluwajana Dokun ； Erbug Celebi

《International Journal of Scientific Research in Information Systems and Engineering (IJSRISE)》 1卷2期 (2015/12) Pp. 57-64

並列摘要

In this study we have evaluated the existing methods of automatic document summarization system and we proposed two approaches in English documents that are based on Latent semantic analysis. Summary selection four existing and two proposed methods for automatic summarization are also used. The evaluated methods that are used include Gong and Liu, Steinberger and Jezek, Murray, Renal & Chaletta, Cross approach and the proposed methods are avesvd and ravesvd. Latent semantic analysis (LSA) is a technique that uses vectorial semantics, for analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA brings out latent relationships within a collection of documents rather than looking at each document isolated from the others. It looks at all the documents as a whole and the terms they contain to identify relationships between them. We have compared the performance of our systems with existing systems in the literature which was developed for this document summarization. The document set used for evaluation of our system is the Document Understanding Conferences (DUC) datasets are document summaries on corpus DUC-2002 and 2004. The evaluation and comparisons of the summaries are performed with ROUGE-L.

並列關鍵字

Abstractive Summarization ； Extractive Summarization ； Information Retrieval ； Latent Semantic Analysis