  This paper describes Macquarie University's contribution to the BioASQ
Challenge (BioASQ 6b, Phase B). We focused on the extraction of the ideal
answers, and the task was approached as an instance of query-based
multi-document summarisation. In particular, this paper focuses on the
experiments related to the deep learning and reinforcement learning approaches
used in the submitted runs. The best run used a deep learning model under a
regression-based framework. The deep learning architecture used features
derived from the output of LSTM chains on word embeddings, plus features based
on similarity with the query, and sentence position. The reinforcement learning
approach was a proof-of-concept prototype that trained a global policy using
REINFORCE. The global policy was implemented as a neural network that used
$tf.idf$ features encoding the candidate sentence, question, and context.
