A Decomposition-based Multi-Objective Optimization Approach for Extractive Multi-Document Text Summarization

Abstract

Currently, due to the over ow of textual information on the Internet, automatic text summarization methods are becoming increasingly important in many elds of knowledge. Extractive multi-document text summarization approaches are intended to automatically generate summaries from a document collection, covering the main content and avoiding redundant information. These approaches can be addressed through optimization techniques. In the scienti c literature, most of them are single-objective optimization approaches, but recently multi-objective approaches have been developed and they have improved the single-objective existing results. In addition, in the eld of multi-objective optimization, decomposition-based approaches are being successfully applied increasingly. For this reason, a Multi-Objective Arti cial Bee Colony algorithm based on Decomposition (MOABC/D) is proposed to solve the extractive multi-document text summarization problem. An asynchronous parallel design of MOABC/D algorithm has been implemented in order to take advantage of multi-core architectures. Experiments have been carried out with Document Understanding Conferences (DUC) datasets, and the results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The obtained results have improved the existing ones in the scienti c literature for ROUGE-1, ROUGE-2, and ROUGE-L scores, also reporting a very good speedup.