@inproceedings{molla-2017-macquarie,
    title = "{M}acquarie {U}niversity at {B}io{ASQ} 5b {--} Query-based Summarisation Techniques for Selecting the Ideal Answers",
    author = "Moll{\'a}, Diego",
    editor = "Cohen, Kevin Bretonnel  and
      Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2017",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada,",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-2308/",
    doi = "10.18653/v1/W17-2308",
    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."
}