@InProceedings{molla:2017:BioNLP17,
  author    = {Molla, Diego},
  title     = {Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {67--75},
  abstract  = {Macquarie University's contribution to the BioASQ challenge (Task 5b Phase B)
	focused on the use of query-based extractive summarisation techniques for the
	generation of the ideal answers. Four runs were submitted, with approaches
	ranging from a trivial system that selected the first $n$ snippets, to the use
	of deep learning approaches under a regression framework. Our experiments and
	the ROUGE results of the five test batches of BioASQ indicate surprisingly good
	results for the trivial approach. Overall, most of our runs on the first three
	test batches achieved the best ROUGE-SU4 results in the challenge.},
  url       = {http://www.aclweb.org/anthology/W17-2308}
}