@inproceedings{kaur-molla-2018-supervised,
    title = "Supervised Machine Learning for Extractive Query Based Summarisation of Biomedical Data",
    author = "Kaur, Mandeep  and
      Moll{\'a}, Diego",
    editor = "Lavelli, Alberto  and
      Minard, Anne-Lyse  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis",
    month = oct,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-5604/",
    doi = "10.18653/v1/W18-5604",
    pages = "29--37",
    abstract = "The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available online. This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries. In particular, we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge. We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation."
}