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

Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation