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10.1016/j.jbi.2014.06.009 http://scihub22266oqcxt.onion/10.1016/j.jbi.2014.06.009 C4261035!4261035!25016293     free     free     free Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomed+Inform 2014 ; ä (ä): 457-67 Nephropedia Template TP {{tp|p=25016293|t=2014. Text Summarization in the Biomedical Domain: A Systematic Review of Recent Research |pdf=|usr=}}{{25016293}} gab.com Text Text Summarization in the Biomedical Domain: A Systematic Review of Recent Research JO: J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=25016293 #FOAvip 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. Twit Text FOAVip Text Summarization in the Biomedical Domain: A Systematic Review of Recent Research ????J Biomed Inform http://www.kidney.de/pget.php?&tw=1&pmid=25016293 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25016293 #FOAvip English Wikipedia <ref>{{Cite pmid|25016293}}</ref> Text Summarization in the Biomedical Domain: A Systematic Review of Recent Research #MMPMID25016293 Mishra R; Bian J; Fiszman M; Weir CR; Jonnalagadda S; Mostafa J; Fiol GD J Biomed Inform 2014[Dec]; ä (ä): 457-67 PMID25016293show ga 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. äText Summarization in the Biomedical Domain: A Systematic Review of Re(epmc).pdf DeepDyve Pubget Overpricing