@InProceedings{shafieibavani-EtAl:2016:COLING,
  author    = {ShafieiBavani, Elaheh  and  Ebrahimi, Mohammad  and  Wong, Raymond  and  Chen, Fang},
  title     = {Appraising UMLS Coverage for Summarizing Medical Evidence},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {513--524},
  abstract  = {When making clinical decisions, practitioners need to rely on the most relevant
	evidence available. However, accessing a vast body of medical evidence and
	confronting with 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, our aim is 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 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.},
  url       = {http://aclweb.org/anthology/C16-1050}
}