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Summarising text with a genetic algorithm-based sentence extraction

Summarising text with a genetic algorithm-based sentence extraction Automatic text summarisation has long been studied and used. The growth in the amount of information on the web results in more demands for automatic methods for text summarisation. Designing a system to produce human-quality summaries is difficult and therefore, many researchers have focused on sentence or paragraph extraction, which is a kind of summarisation. In this paper, we introduce a new method to make such extracts. Genetic Algorithm (GA)-based sentence selection is used to make a summary, and once the summary is created, it is evaluated using a fitness function. The fitness function is based on three following factors: Readability Factor (RF), Cohesion Factor (CF) and Topic-Relation Factor (TRF). In this paper, we introduce these factors and discuss the Genetic Algorithm with the specific fitness function. Evaluation results are also shown and discussed in the paper. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge Management Studies Inderscience Publishers

Summarising text with a genetic algorithm-based sentence extraction

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1743-8268
eISSN
1743-8276
DOI
10.1504/IJKMS.2008.01975
Publisher site
See Article on Publisher Site

Abstract

Automatic text summarisation has long been studied and used. The growth in the amount of information on the web results in more demands for automatic methods for text summarisation. Designing a system to produce human-quality summaries is difficult and therefore, many researchers have focused on sentence or paragraph extraction, which is a kind of summarisation. In this paper, we introduce a new method to make such extracts. Genetic Algorithm (GA)-based sentence selection is used to make a summary, and once the summary is created, it is evaluated using a fitness function. The fitness function is based on three following factors: Readability Factor (RF), Cohesion Factor (CF) and Topic-Relation Factor (TRF). In this paper, we introduce these factors and discuss the Genetic Algorithm with the specific fitness function. Evaluation results are also shown and discussed in the paper.

Journal

International Journal of Knowledge Management StudiesInderscience Publishers

Published: Jan 1, 2008

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