Resolving ambiguity in biomedical text to improve summarization

Access to the vast body of research literature that is now available on biomedicine and related fields can be improved with automatic summarization. This paper describes a summarization system for the biomedical domain that represents documents as graphs formed from concepts and relations in the UMLS Metathesaurus. This system has to deal with the ambiguities that occur in biomedical documents. We describe a variety of strategies that make use of MetaMap and Word Sense Disambiguation (WSD) to accurately map biomedical documents onto UMLS Metathesaurus concepts. Evaluation is carried out using a collection of 150 biomedical scientific articles from the BioMed Central corpus. We find that using WSD improves the quality of the summaries generated.

► Using rich semantic representations improves automatic summarization.
► Summarization using external knowledge sources introduces the problem of ambiguity.
► Biomedical summarization using UMLS is affected by lexical ambiguity.
► Using WSD significantly improves the quality of the summaries.