Currently, due to the overflow of textual information on the Internet, automatic text summarization methods are becoming increasingly important in many fields 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 scientific 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 field of multi-objective optimization, decomposition-based approaches are being successfully applied increasingly. For this reason, a Multi-Objective Artificial 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 scientific literature for ROUGE-1, ROUGE-2, and ROUGE-L scores, also reporting a very good speedup.

当前，由于互联网上文​​本信息的溢出，自动文本摘要方法在许多知识领域中变得越来越重要。抽取式多文档文本摘要方法旨在从文档集合自动生成摘要，该摘要涵盖主要内容并避免冗余信息。这些方法可以通过优化技术来解决。在科学文献中，大多数都是单目标优化方法，但是最近已经开发了多目标方法，并且它们改善了现有的单目标结果。另外，在多目标优化领域，基于分解的方法正在越来越多地成功应用。为此原因，提出了一种基于分解的多目标人工蜂群算法（MOABC / D），以解决多文档文本的提取问题。为了利用多核体系结构，已经实现了MOABC / D算法的异步并行设计。已使用文档理解会议（DUC）数据集进行了实验，并已使用面向召回评估的迷信评估（ROUGE）指标对结果进行了评估。获得的结果改善了科学文献中ROUGE-1，ROUGE-2和ROUGE-L分数的现有结果，并且报告了非常好的加速效果。为了利用多核体系结构，已经实现了MOABC / D算法的异步并行设计。已使用文档理解会议（DUC）数据集进行了实验，并使用面向召回评估的迷信评估（ROUGE）指标对结果进行了评估。获得的结果改善了科学文献中ROUGE-1，ROUGE-2和ROUGE-L分数的现有结果，并且报告了非常好的加速效果。为了利用多核体系结构，已经实现了MOABC / D算法的异步并行设计。已使用文档理解会议（DUC）数据集进行了实验，并已使用面向召回评估的迷信评估（ROUGE）指标对结果进行了评估。获得的结果改善了科学文献中ROUGE-1，ROUGE-2和ROUGE-L分数的现有结果，并且报告了非常好的加速效果。