title: Text summarization in the biomedical domain: a systematic review of recent research.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
authors.forename: Rashmi
authors.initials: R
authors.lastname: Mishra
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; Clinical Modeling Team, Intermountain Healthcare, Salt Lake City, UT, USA.
authors.forename: Jiantao
authors.initials: J
authors.lastname: Bian
authors.affiliation: Lister Hill Center, National Library of Medicine, Bethesda, MD, USA.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA; VA Medical Center, Salt Lake City, UT, USA.
authors.forename: Charlene R
authors.initials: CR
authors.lastname: Weir
authors.affiliation: Department of Preventive Medicine-Health and Biomedical Informatics, Northwestern University, Chicago, IL, USA.
authors.forename: Siddhartha
authors.initials: S
authors.lastname: Jonnalagadda
authors.affiliation: School of Information and Library Science (SILS), University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Javed
authors.initials: J
authors.lastname: Mostafa
authors.affiliation: Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA. Electronic address: guilherme.delfiol@utah.edu.
authors.forename: Guilherme
authors.initials: G
authors.lastname: Del Fiol
mesh_terms: D000043:Abstracting and Indexing; D001185:Artificial Intelligence; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing
keywords: Biomedical domain; Intrinsic evaluation; Language processing; Machine learning; Text summarization