TEXT SUMMARIZATION USING PARTICLE SWARM OPTIMIZATION ALGORITHM

Abstract

Automatic text summarization is the process of creating a small version from the original text. Extraction approach is one of way of extracting the most important sentences in document, this approach is used to select sentences after calculating the score for each sentence, and based on user defined summary ratio the top n sentences are selected as summary. The selection of the informative sentence is a challenge for extraction based automatic text summarization researchers. This research applied extraction based automatic single document text summarization method using the particle swarm optimization algorithm to find the best feature weight score to differentiate between important and non important feature. The Recall-Oriented Understanding for Gisting Evaluation (ROUGE) toolkit was used for measuring performance. DUC 2002 data sets provided by the Document Understanding Conference 2002 were used in the evaluation process. The summary that generated by PSO algorithm was compared with other algorithm (GA,ACO) and used DE algorithm as benchmark. Experimental results showed that the summaries produced by the DE algorithm are better than another algorithm