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dc.contributor.authorWang, Emily S.
dc.date.accessioned2019-03-26T11:07:54Z
dc.date.created2018-05
dc.date.issued2018-06-29
dc.date.submitted2018
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:38811563*
dc.description.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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseMETA_ONLY
dc.subjectComputer Science
dc.subjectBiology, General
dc.titlePointing to On-Point Answers: An Approach for Query-Based Biomedical Summarization
dc.typeThesis or Dissertation
dash.depositing.authorWang, Emily S.
dash.embargo.until10000-01-01
dc.date.available2019-03-26T11:07:54Z
thesis.degree.date2018
thesis.degree.grantorHarvard College
thesis.degree.levelUndergraduate
thesis.degree.nameAB
dc.type.materialtext
thesis.degree.departmentComputer Science
dash.identifier.vireohttp://etds.lib.harvard.edu/college/admin/view/297
dash.author.emailemilysmwang@gmail.com


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