An Indicator-based Multi-Objective Optimization Approach Applied to Extractive Multi-Document Text Summarization

Abstract

The massive amount of textual information on the Internet makes that automatic text summarization methods are becoming very important nowadays. Particularly, the purpose of extractive multi-document text summarization methods is to generate summaries from a document collection by, simultaneously, covering the main content and reducing the redundant information. In the scientific literature, these summarization methods have been addressed through optimization techniques, being almost all of them single-objective optimization approaches. Nevertheless, multi-objective approaches have gained importance because their results have improved the single-objective ones.On the other hand, in the multi-objective optimization field, indicator-based approaches have obtained good results in other applications. For this reason, an Indicator-based Multi-Objective Artificial Bee Colony (IMOABC) algorithm has been developed and applied to the extractive multi-document text summarization problem. Experiments have been carried out based on Document Understanding Conferences (DUC) datasets, and the obtained results have been evaluated and compared with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have improved to the ones in the scientific literature between 7.37% and 40.76% and 2.59% and 11.24% for ROUGE-2 and ROUGE-L, respectively.