Modeling the user in natural language systems
Computational Linguistics - Special issue on user modeling
Constructing literature abstracts by computer: techniques and prospects
Information Processing and Management: an International Journal - Special issue on natural language processing and information retrieval
The mythical man-month (anniversary ed.)
The mythical man-month (anniversary ed.)
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Hypermedia exploration with interactive dynamic maps
International Journal of Human-Computer Studies - Special issue: knowledge-based hypermedia
A knowledge-based approach to deriving logical structure from document images
A knowledge-based approach to deriving logical structure from document images
Building natural language generation systems
Building natural language generation systems
Summarizing Similarities and Differences Among Related Documents
Information Retrieval
Making Use: Scenario-Based Design of Human-Computer Interactions
Making Use: Scenario-Based Design of Human-Computer Interactions
The Evaluation of a Personalised Health Information System for Patients with Cancer
User Modeling and User-Adapted Interaction
Iterative User-Interface Design
Computer
Automatic discovery of logical document structure
Automatic discovery of logical document structure
Automatic text summarization as applied to information retrieval: using indicative and informative summaries
Lexical cohesion computed by thesaural relations as an indicator of the structure of text
Computational Linguistics
Generating natural language summaries from multiple on-line sources
Computational Linguistics - Special issue on natural language generation
PHRED: a generator for natural language interfaces
Computational Linguistics
Partial parsing via finite-state cascades
Natural Language Engineering
A common theory of information fusion from multiple text sources step one: cross-document structure
SIGDIAL '00 Proceedings of the 1st SIGdial workshop on Discourse and dialogue - Volume 10
Journal of Biomedical Informatics
Answering Clinical Questions with Knowledge-Based and Statistical Techniques
Computational Linguistics
BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis
An approach to summarizing short stories
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Individual and domain adaptation in sentence planning for dialogue
Journal of Artificial Intelligence Research
Methodological Review: What can natural language processing do for clinical decision support?
Journal of Biomedical Informatics
Journal of Biomedical Informatics
LNLBioNLP '06 Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology
Computational Linguistics
Adaptive information for consumers of healthcare
The adaptive web
AskHERMES: An online question answering system for complex clinical questions
Journal of Biomedical Informatics
Summarization of clinical information: A conceptual model
Journal of Biomedical Informatics
International Journal of Data Mining and Bioinformatics
Hi-index | 0.00 |
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.