  This paper presents the participation of Macquarie University and the
Australian National University for Task B Phase B of the 2020 BioASQ Challenge
(BioASQ8b). Our overall framework implements Query focused multi-document
extractive summarisation by applying either a classification or a regression
layer to the candidate sentence embeddings and to the comparison between the
question and sentence embeddings. We experiment with variants using BERT and
BioBERT, Siamese architectures, and reinforcement learning. We observe the best
results when BERT is used to obtain the word embeddings, followed by an LSTM
layer to obtain sentence embeddings. Variants using Siamese architectures or
BioBERT did not improve the results.
