TEXT SUMMARIZATION USING ANT COLONY OPTIMIZATION ALGORITHM

Abstract

Automatic text summarization plays increasingly an important role with the exponential growth of documents on the Web. Numerous approaches have been proposed to identify important contents for automatic text summarization. Sentence scoring approaches mark scores for input sentences rank all of them decadently. Only higher ranked sentences are selected to be part of the summary. Find the informative sentences is an important issue for the researchers in an extractive based automatic text summarization. This research aim to use extraction based automatic single document text summarization method using evolutionary algorithm called Ant Colony Optimization algorithm ACO to produce good summaries. We use ACO algorithm to find out the best sub set feature weight score. To the best of our knowledge has never been used for solving text summarization problem before. To evaluate the proposed method standard dataset from Document Understanding Conference (DUC) 2002 in used and the Recall- Oriented Understudy for Gisting Evaluation (ROUGE) as the standard evaluation measurement toolkit is used .Set of evolutionary algorithms are used in this research in term of evolutionary the experimental results showed our proposed method has performed well compared with algorithms (Particle Swarm Optimization methods and Genetic Algorithm). Although our targeted ACO algorithm is select to improve the performance of text summarization has not out performance the latest proposed method (Differential Evolution) but performance satisfactory