Customization in a unified framework for summarizing medical literature.
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
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.
Elhadad
N
N
Department of Computer Science, Columbia University, New York, NY 10027, USA. noemie@cs.columbia.edu
Kan
M-Y
MY
Klavans
J L
JL
McKeown
K R
KR
D000043:Abstracting and Indexing; D003199:Computer Systems; D019992:Databases as Topic; D001330:Electronic Data Processing; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008490:Medical Informatics; D016571:Neural Networks, Computer; D039781:PubMed