Generating automatic summaries with an indicator and decomposition-based hybrid evolutionary approach

• IDHEA (Indicator and Decomposition-based Hybrid Evolutionary Algorithm) is proposed. • IDHEA is designed, implemented, and applied to solve automatic text summarization. • IDHEA’s performance is assessed by multi-objective and text summarization metrics. • IDHEA is compared with multi-objective algorithms and proposals from other authors. • IDHEA outperforms these other optimization algorithms from the scientific literature. The field of multi-objective optimization is experiencing a relevant growth due to its successful applications in numerous real-life problems. Two prominent trends are indicator-based and decomposition-based search strategies. However, the performance of these search strategies depends on the specific problem to solve. In the context of automatic summarization, hybridization of these techniques is an interesting and challenging proposal, which aims to improve the performance. For this reason, an Indicator and Decomposition-based Hybrid Evolutionary Algorithm (IDHEA) has been designed, implemented, and tested for addressing the automatic summarization problem. The proposed hybrid multi-objective approach has integrated the fundamentals of both indicator-based and decomposition-based techniques to solve this particular problem. Experimentation has been conducted using Document Understanding Conferences (DUC) dataset. The performance has been assessed by means of multi-objective metrics such as hypervolume, inverted generational distance, distance to ideal point, and set coverage, while summary quality has been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The proposed approach has outperformed standard algorithms in multi-objective evaluation, in addition to improving the existing results in the scientific literature in terms of summary quality.