Hybridization of Social Spiders and Extractions Techniques for Automatic Text Summaries

Abstract

The authors propose a new multilayer approach for automatic text summaries. In the first layer, they use two techniques of extraction, one after the other: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the second layer aims to optimize the results of the previous layer by a meta-heuristic based on social spiders. Its objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text, minimize the sum of scores in order to increase the summarization rate; this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final layer concerned in choosing a best summary from all candidates summaries generated by optimization layer, we opted for the technique of voting with a simple majority.