A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study

Automatic text summarization is a topic of great interest in many fields knowledge. Particularly, query-oriented extractive multi-document methods have increased their importance recently, since they can automatically generate summary according to query given by the user. One way address this problem multi-objective optimization approaches. In paper, memetic algorithm, specifically Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied solve problem. Experiments conducted with datasets from Text Analysis Conference (TAC), obtained results evaluated Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The shown that proposed approach achieved important improvements respect works scientific literature. Specifically, 25.41%, 7.13%, 30.22% percentage ROUGE-1, ROUGE-2, ROUGE-SU4 scores respectively reached. addition, MOSFLA medicine texts Topically Diverse Query Focus Summarization (TD-QFS) dataset as case study.