Combine clustering and frequent itemsets mining to enhance biomedical text summarization
作者：
Highlights：
• A new biomedical text summarization based on clustering and frequent itemsets mining.
• Biomedical texts are represented using concepts instead of terms.
• This combination enhances the quality of the generated summaries.
• The clustering has a crucial impact on the discovered frequent itemsets.
• Contribute all clusters in the sentence selection step yields to better performances.
摘要
•A new biomedical text summarization based on clustering and frequent itemsets mining.•Biomedical texts are represented using concepts instead of terms.•This combination enhances the quality of the generated summaries.•The clustering has a crucial impact on the discovered frequent itemsets.•Contribute all clusters in the sentence selection step yields to better performances.
论文关键词：Biomedical text summarization,Biomedical concepts,Clustering,Frequent itemsets mining,ROUGE metrics
论文评审过程：Received 20 October 2018, Revised 31 May 2019, Accepted 2 June 2019, Available online 3 June 2019, Version of Record 20 June 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.06.002
