Different approaches for identifying important concepts in probabilistic biomedical text summarization
作者：
Highlights：
• Introducing a Bayesian summarizer for biomedical text documents.
• Different feature selection approaches for identifying important concepts in a biomedical text.
• The efficiency of feature selection methods are evaluated on the performance of the Bayesian text summarizer.
• The distribution of important concepts is used to classify the sentences of an input document.
• The summarizer outperforms other frequency-based, domain-independent and baseline methods.
摘要
•Introducing a Bayesian summarizer for biomedical text documents.•Different feature selection approaches for identifying important concepts in a biomedical text.•The efficiency of feature selection methods are evaluated on the performance of the Bayesian text summarizer.•The distribution of important concepts is used to classify the sentences of an input document.•The summarizer outperforms other frequency-based, domain-independent and baseline methods.
论文关键词：Medical text mining,Data mining,Bayesian classification,Feature selection,UMLS concept,Sentence classification
论文评审过程：Received 4 October 2016, Revised 25 August 2017, Accepted 28 November 2017, Available online 6 December 2017, Version of Record 5 February 2018.
论文官网地址：https://doi.org/10.1016/j.artmed.2017.11.004
