COVIDSum: A linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers.

Cai, Xiaoyan; Liu, Sen; Yang, Libin; Lu, Yan; Zhao, Jintao; Shen, Dinggang; Liu, Tianming

Cai, Xiaoyan; Liu, Sen; Yang, Libin; Lu, Yan; Zhao, Jintao; Shen, Dinggang; Liu, Tianming.

Afiliação

Cai X; School of Automation, Northwestern Polytechnic University, Xi'an 710072, Shaanxi, People's Republic of China. Electronic address: xiaoyanc@nwpu.edu.cn.
Liu S; School of Automation, Northwestern Polytechnic University, Xi'an 710072, Shaanxi, People's Republic of China.
Yang L; School of Automation, Northwestern Polytechnic University, Xi'an 710072, Shaanxi, People's Republic of China.
Lu Y; Department of Cardiovascular Diseases, Xidian Group Hospital, Xi'an 710077, Shannxi, People's Republic of China.
Zhao J; School of Automation, Northwestern Polytechnic University, Xi'an 710072, Shaanxi, People's Republic of China.
Shen D; School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, People's Republic of China.
Liu T; Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30602, USA.

J Biomed Inform ; 127: 103999, 2022 03.

Article em En | MEDLINE | ID: mdl-35104642

Assuntos

COVID-19; Humanos; Idioma; Editoração; SARS-CoV-2

Palavras-chave

Abstractive summarization; COVID-19 scientific papers; Linguistically enriched pre-trained language model; SciBERT

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article

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