Summarization of an online medical encyclopedia

Marcelo Fiszman, Thomas C. Rindflesch, Halil Kilicoglu

Research output: Contribution to journalArticlepeer-review


We explore a knowledge-rich (abstraction) approach to summarization and apply it to multiple documents from an online medical encyclopedia. A semantic processor functions as the source interpreter and produces a list of predications. A transformation stage then generalizes and condenses this list, ultimately generating a conceptual condensate for a given disorder topic. We provide a preliminary evaluation of the quality of the condensates produced for a sample of four disorders. The overall precision of the disorder conceptual condensates was 87%, and the compression ratio from the base list of predications to the final condensate was 98%. The conceptual condensate could be used as input to a text generator to produce a natural language summary for a given disorder topic.

Original languageEnglish (US)
Pages (from-to)506-510
Number of pages5
JournalStudies in health technology and informatics
StatePublished - Jan 1 2004
Externally publishedYes


  • Automatic Summarization
  • Knowledge Representation
  • Natural Language Processing

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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