A multi-objective memetic algorithm for query-oriented text summarization: Medicine texts as a case study
作者：
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.
摘要
•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.
论文关键词：Query-oriented summarization,Multi-objective optimization,Memetic algorithm,Recall-oriented understudy for gisting evaluation,Medicine texts
论文评审过程：Received 18 December 2020, Revised 24 November 2021, Accepted 25 February 2022, Available online 12 March 2022, Version of Record 23 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116769
