USING GENETIC ALGORITHMS WITH LEXICAL CHAINS FOR AUTOMATIC TEXT SUMMARIZATION

Automatic text summarization takes an input text and extracts the most important content in the text. Determining the importance depends on several factors. In this paper, we combine two different approaches that have been used in text summarization. The first one is using genetic algorithms to learn the patterns in the documents that lead to the summaries. The other one is using lexical chains as a representation of the lexical cohesion that exists in the text. We propose a novel approach that incorporates lexical chains into the model as a feature and learns the feature weights by genetic algorithms. The experiments showed that combining different types of features and also including lexical chains outperform the classical approaches.