A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study. (15th July 2022) - Sanchez-Gomez, Jesus M.; Vega-Rodríguez, Miguel A.; Pérez, Carlos J.
Abstract: Automatic text summarization is a topic of great interest in many fields of knowledge. Particularly, query-oriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user. One way to address this problem is by multi-objective optimization approaches. In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarization problem. Experiments have been conducted with datasets from Text Analysis Conference (TAC), and the obtained results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have shown that the proposed approach has achieved important improvements with respect to the works of scientific literature. Specifically, 25.41%, 7.13%, and 30.22% of percentage improvements in ROUGE-1, ROUGE-2, and ROUGE-SU4 scores have been respectively reached. In addition, MOSFLA has been applied to medicine texts from the Topically Diverse Query Focus Summarization (TD-QFS) dataset as a case study. Highlights: The query-oriented text summarization problem is addressed. The criteria of query relevance and redundancy reduction are optimized simultaneously. A multi-objective memetic algorithm (MOSFLA) is designed and developed. The experiments use the Text Analysis Conference datasets and the ROUGE metrics. MOSFLA outperforms the results of other 12 approaches from the scientific literature.