Appraising UMLS Coverage for Summarizing Medical Evidence

摘要

When making clinical decisions, practitioners need to rely on the most relevant evidence available. However, accessing a vast body of medical evidence and confronting the issue of information overload, can be challenging and time consuming. This paper proposes an effective sum-marizer for medical evidence by utilizing both UMLS and WordNet. Given a clinical query and a set of relevant abstracts, we aim to generate a fluent, well-organized, and compact summary that answers the query. Analysis via ROUGE metrics shows that using WordNet as a general-purpose lexicon helps to capture the concepts not covered by the UMLS Metathesaurus, and hence significantly increases the summarization performance. The effectiveness of our proposed approach is demonstrated by conducting a set of experiments over a specialized evidence-based medicine (EBM) corpus - which has been gathered and annotated for the purpose of biomedical text summarization.