Text summarization in the biomedical domain: A systematic review of recent research

• Published in: Journal of biomedical informatics 2014-12, Vol.52, p.457-467
• Main Authors: Mishra, Rashmi, Bian, Jiantao, Fiszman, Marcelo, Weir, Charlene R., Jonnalagadda, Siddhartha, Mostafa, Javed, Del Fiol, Guilherme
• Format: Article
• Language: English
• Subjects: Abstracting and Indexing as Topic; Artificial Intelligence; Biomedical domain; Biomedical materials; Criteria; Digital systems; Humans; Inclusions; Information Storage and Retrieval - methods; Intrinsic evaluation; Language processing; Literature reviews; Machine learning; MEDLINE; Natural Language Processing; Text summarization; Texts
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2014.06.009

[Display omitted] •First systematic review of text summarization in the biomedical domain.•The study found a predominance of methods producing extractive summaries.•Multiple documents were used as the source for summarization.•Natural language processing, and hybrid techniques were prominently used.•Research is needed on the application of text summarization in real settings. The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. Recent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.