Appraising UMLS Coverage for Summarizing Medical Evidence

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 summarizer 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 generalpurpose 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.