Pointing to On-Point Answers: An Approach for Query-Based Biomedical Summarization
Access StatusFull text of the requested work is not available in DASH at this time ("restricted access"). For more information on restricted deposits, see our FAQ.
Wang, Emily S.
MetadataShow full item record
AbstractIn my thesis, I explored the potential of deep learning for query-based biomedical summarization. Summary questions from the BioASQ Dataset, derived from PubMed research abstracts, pose challenges for deep learning-based methods due to their domain specificity, multi-span ideal answers, and limited number of training examples. Previous approaches for answering biomedical questions consist of transfer learning from large neural QA models to answer list/factoid questions and traditional statistical methods with careful, domain-specific feature engineering to answer summary questions. To our knowledge, current strategies for answering biomedical summary questions, which are more complex, have yet to use end-to-end deep learning. I proposed a new approach for query-based biomedical summarization: Biomedical Pointer Network (Biomed-Ptr). Biomed-Ptr modifies Pointer Network to use question-biased attention, a continuous bag of words sentence representation instead of the standard RNN-based encoding, and biomedical word2vec embeddings. Biomed-Ptr is an exploratory framework that sheds light on tackling query-based biomedical summarization using sentence ranking with an attentional neural model, which enables multi-span answering and reduces model complexity.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:38811563
- FAS Theses and Dissertations