57 Text Summarization Based on Genetic Programming

Abstract

This work proposes an approach to address the problem of improving content selection in automatic text summarization by using some statistical tools. This approach is a trainable summarizer, which takes into account several features, for each sentence to generate summaries. First, we investigate the effect of each sentence feature on the summarization task. Then we use all features in combination to train genetic programming (GP), vector approach and fuzzy approach in order to construct a text summarizer for each model. Furthermore, we use trained models to test summarization performance. The proposed approach performance is measured at several compression rates on a data corpus composed of 17 English scientific articles.