Customization in a unified framework for summarizing medical literature. Elhadad, N., Kan, M., Klavans, J., L., & McKeown, K., R. Artificial intelligence in medicine, 33(2):179-98, 2, 2005.
Customization in a unified framework for summarizing medical literature. [pdf]Paper  abstract   bibtex   
OBJECTIVE: 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.
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 title = {Customization in a unified framework for summarizing medical literature.},
 type = {article},
 year = {2005},
 identifiers = {[object Object]},
 keywords = {Abstracting and Indexing as Topic,Automatic Data Processing,Computer Systems,Databases as Topic,Humans,Information Storage and Retrieval,Information Storage and Retrieval: methods,Medical Informatics,Neural Networks (Computer),PubMed},
 created = {2013-10-09T05:48:36.000Z},
 pages = {179-98},
 volume = {33},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/15811784},
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 abstract = {OBJECTIVE: 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.},
 bibtype = {article},
 author = {Elhadad, N and Kan, M-Y and Klavans, J L and McKeown, K R},
 journal = {Artificial intelligence in medicine},
 number = {2}
}
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