Text Summarization in the Biomedical Domain: a Systematic Review of Recent Research

Overview

Journal J Biomed Inform

Publisher Elsevier

Specialty Medical Informatics

Date 2014 Jul 13

PMID 25016293

Citations 50

Authors

Rashmi Mishra

Jiantao Bian

Marcelo Fiszman

Charlene R Weir

Siddhartha Jonnalagadda

Javed Mostafa

Guilherme Del Fiol

Affiliations

Soon will be listed here.

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.

Materials And 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.

Discussion: 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.

Conclusion: 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.

Citing Articles

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References

Sarker A, Molla D, Paris C . Extractive summarisation of medical documents using domain knowledge and corpus statistics. Australas Med J. 2012; 5(9):478-81. PMC: 3477776. DOI: 10.4066/AMJ.2012.1361. View

Kushniruk A, McKeown K, Klavans J, Jordan D, Laflamme M, Patel V . Usability evaluation of an experimental text summarization system and three search engines: implications for the reengineering of health care interfaces. Proc AMIA Symp. 2002; :420-4. PMC: 2244493. View

Plaza L, Diaz A, Gervas P . A semantic graph-based approach to biomedical summarisation. Artif Intell Med. 2011; 53(1):1-14. DOI: 10.1016/j.artmed.2011.06.005. View

Davidoff F, Miglus J . Delivering clinical evidence where it's needed: building an information system worthy of the profession. JAMA. 2011; 305(18):1906-7. DOI: 10.1001/jama.2011.619. View

Zhang H, Fiszman M, Shin D, Wilkowski B, Rindflesch T . Clustering cliques for graph-based summarization of the biomedical research literature. BMC Bioinformatics. 2013; 14:182. PMC: 3682874. DOI: 10.1186/1471-2105-14-182. View

Fiszman M, Rindflesch T, Kilicoglu H . Summarizing drug information in Medline citations. AMIA Annu Symp Proc. 2007; :254-8. PMC: 1839479. View

Rindflesch T, Fiszman M . The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform. 2004; 36(6):462-77. DOI: 10.1016/j.jbi.2003.11.003. View

Smith R . Strategies for coping with information overload. BMJ. 2010; 341:c7126. DOI: 10.1136/bmj.c7126. View

Pluye P, Grad R, Dunikowski L, Stephenson R . Impact of clinical information-retrieval technology on physicians: a literature review of quantitative, qualitative and mixed methods studies. Int J Med Inform. 2005; 74(9):745-68. DOI: 10.1016/j.ijmedinf.2005.05.004. View

10.

Fiszman M, Demner-Fushman D, Kilicoglu H, Rindflesch T . Automatic summarization of MEDLINE citations for evidence-based medical treatment: a topic-oriented evaluation. J Biomed Inform. 2008; 42(5):801-13. PMC: 2776079. DOI: 10.1016/j.jbi.2008.10.002. View

11.

Workman T, Fiszman M, Hurdle J . Text summarization as a decision support aid. BMC Med Inform Decis Mak. 2012; 12:41. PMC: 3461485. DOI: 10.1186/1472-6947-12-41. View

12.

Aronson A, Lang F . An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc. 2010; 17(3):229-36. PMC: 2995713. DOI: 10.1136/jamia.2009.002733. View

13.

Elhadad N, Kan M, Klavans J, McKeown K . Customization in a unified framework for summarizing medical literature. Artif Intell Med. 2005; 33(2):179-98. DOI: 10.1016/j.artmed.2004.07.018. View

14.

Cao Y, Liu F, Simpson P, Antieau L, Bennett A, Cimino J . AskHERMES: An online question answering system for complex clinical questions. J Biomed Inform. 2011; 44(2):277-88. PMC: 3433744. DOI: 10.1016/j.jbi.2011.01.004. View

15.

Shang Y, Li Y, Lin H, Yang Z . Enhancing biomedical text summarization using semantic relation extraction. PLoS One. 2011; 6(8):e23862. PMC: 3162578. DOI: 10.1371/journal.pone.0023862. View

16.

Plaza L, Carrillo-de-Albornoz J . Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization. BMC Bioinformatics. 2013; 14:71. PMC: 3648362. DOI: 10.1186/1471-2105-14-71. View

17.

Johnson D, Zou Q, Dionisio J, Liu V, Chu W . Modeling medical content for automated summarization. Ann N Y Acad Sci. 2003; 980:247-58. DOI: 10.1111/j.1749-6632.2002.tb04901.x. View

18.

Niu Y, Zhu X, Hirst G . Using outcome polarity in sentence extraction for medical question-answering. AMIA Annu Symp Proc. 2007; :599-603. PMC: 1839454. View

19.

Savova G, Masanz J, Ogren P, Zheng J, Sohn S, Kipper-Schuler K . Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010; 17(5):507-13. PMC: 2995668. DOI: 10.1136/jamia.2009.001560. View

20.

Zhang H, Fiszman M, Shin D, Miller C, Rosemblat G, Rindflesch T . Degree centrality for semantic abstraction summarization of therapeutic studies. J Biomed Inform. 2011; 44(5):830-8. PMC: 3170688. DOI: 10.1016/j.jbi.2011.05.001. View