Abstract
Automatic text summarization takes an input text and extracts the most important content in the text. Determining the importance of information depends on several factors. In this paper, we combine two different approaches that have been used in the text summarization domain. 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 throughout the text. We propose a novel approach that incorporates lexical chains into the model as a feature and learns the feature weights using genetic algorithms. The experiments performed on the CAST corpus showed that combining different classes of features and also including lexical chains outperform the classical approaches.
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