Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.artmed.2004.07.018
DC FieldValue
dc.titleCustomization in a unified framework for summarizing medical literature
dc.contributor.authorElhadad, N.
dc.contributor.authorKan, M.-Y.
dc.contributor.authorKlavans, J.L.
dc.contributor.authorMcKeown, K.R.
dc.date.accessioned2013-07-04T07:39:42Z
dc.date.available2013-07-04T07:39:42Z
dc.date.issued2005
dc.identifier.citationElhadad, N., Kan, M.-Y., Klavans, J.L., McKeown, K.R. (2005). Customization in a unified framework for summarizing medical literature. Artificial Intelligence in Medicine 33 (2) : 179-198. ScholarBank@NUS Repository. https://doi.org/10.1016/j.artmed.2004.07.018
dc.identifier.issn09333657
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39352
dc.description.abstractObjective: We present the summarization system in the PErsonalized Retrieval and Summarization of Images, Video and Language (PERSIVAL) medical digital library. Although we discuss the context of our summarization research within the PERSIVAL platform, the primary focus of this article is on strategies to define and generate customized summaries. Methods and material: Our summarizer employs a unified user model to create a tailored summary of relevant documents for either a physician or lay person. The approach takes advantage of regularities in medical literature text structure and content to fulfill identified user needs. Results: The resulting summaries combine both machine-generated text and extracted text that comes from multiple input documents. Customization includes both group-based modeling for two classes of users, physician and lay person, and individually driven models based on a patient record. Conclusions: Our research shows that customization is feasible in a medical digital library. © 2004 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.artmed.2004.07.018
dc.sourceScopus
dc.subjectClinical information system
dc.subjectMedical digital library
dc.subjectMulti-document information extraction
dc.subjectMulti-document summarization
dc.subjectUser modeling
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.artmed.2004.07.018
dc.description.sourcetitleArtificial Intelligence in Medicine
dc.description.volume33
dc.description.issue2
dc.description.page179-198
dc.description.codenAIMEE
dc.identifier.isiut000228673800006
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