Appraising UMLS Coverage for Summarizing Medical Evidence
Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, Fang Chen
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.- Anthology ID:
- C16-1050
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 513–524
- Language:
- URL:
- https://aclanthology.org/C16-1050
- DOI:
- Cite (ACL):
- Elaheh ShafieiBavani, Mohammad Ebrahimi, Raymond Wong, and Fang Chen. 2016. Appraising UMLS Coverage for Summarizing Medical Evidence. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 513–524, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Appraising UMLS Coverage for Summarizing Medical Evidence (ShafieiBavani et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/naacl24-info/C16-1050.pdf