  The automation of text summarisation of biomedical publications is a pressing
need due to the plethora of information available on-line. 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.
