Text Summarization by using Genetic Algorithm Method‬

Abstract

Automatic text summarization is a process of rewriting text into a shorter compressed version from the original text. Extraction focuses on the selection of particular pieces of text from a document where the sentences and/or phrases with the highest score are considered as salient sentences and are chosen to form the summary. The selection of the informative sentence is a challenge for extraction based automatic text summarization researchers. This research applied an extraction based automatic single document text summarization method help differentiate using the genetic algorithm (GA) to find out the best feature weight score to difference between important and non-important features. The Recall-Oriented Understanding for Gusting Evaluation (ROUGE) toolkit was used for measuring the performance. DUC 2002 data sets provided by the Document Understanding Conference 2002 were used in the evaluation process. The summary that generated by GA algorithm were compared with other evolutionary algorithm (PSO,ACO) and used DE algorithm as benchmark. Experimental results showed that the summaries produced by the DE algorithm are better than other algorithms. In the meantime, recently propped algorithms such as (ACO) could out performance GA.