@InProceedings{chandu-EtAl:2017:BioNLP17,
  author    = {Chandu, Khyathi  and  Naik, Aakanksha  and  Chandrasekar, Aditya  and  Yang, Zi  and  Gupta, Niloy  and  Nyberg, Eric},
  title     = {Tackling Biomedical Text Summarization: OAQA at BioASQ 5B},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {58--66},
  abstract  = {In this paper, we describe our participation in phase B of task 5b of the fifth
	edition of the annual BioASQ challenge, which includes answering factoid, list,
	yes-no and summary questions from biomedical data. We describe our techniques
	with an emphasis on ideal answer generation, where the goal is to produce a
	relevant, precise, non-redundant, query-oriented summary from multiple relevant
	documents. We make use of extractive summarization techniques to address this
	task and experiment with different biomedical ontologies and various algorithms
	including agglomerative clustering, Maximum Marginal Relevance (MMR) and
	sentence compression. We propose a novel word embedding based tf-idf similarity
	metric and a soft positional constraint which improve our system performance.
	We evaluate our techniques on test batch 4 from the fourth edition of the
	challenge. Our best system achieves a ROUGE-2 score of 0.6534 and ROUGE-SU4
	score of 0.6536.},
  url       = {http://www.aclweb.org/anthology/W17-2307}
}