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This paper presents Macquarie University's participation to the two mostrecent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B(BioASQ10b), Phase~B. In these tasks, participating systems are expected togenerate complex answers to biomedical questions, where the answers may containmore than one sentence. We apply query-focused extractive summarisationtechniques. In particular, we follow a sentence classification-based approachthat scores each candidate sentence associated to a question, and the $n$highest-scoring sentences are returned as the answer. The Synergy Taskcorresponds to an end-to-end system that requires document selection, snippetselection, and finding the final answer, but it has very limited training data.For the Synergy task, we selected the candidate sentences following two phases:document retrieval and snippet retrieval, and the final answer was found byusing a DistilBERT/ALBERT classifier that had been trained on the training dataof BioASQ9b. Document retrieval was achieved as a standard search over theCORD-19 data using the search API provided by the BioASQ organisers, andsnippet retrieval was achieved by re-ranking the sentences of the top retrieveddocuments, using the cosine similarity of the question and candidate sentence.We observed that vectors represented via sBERT have an edge over tf.idf.BioASQ10b Phase B focuses on finding the specific answers to biomedicalquestions. For this task, we followed a data-centric approach. We hypothesisedthat the training data of the first BioASQ years might be biased and weexperimented with different subsets of the training data. We observed animprovement of results when the system was trained on the second half of theBioASQ10b training data.

Query-focused Extractive Summarisation for Biomedical and COVID-19 Complex Question Answering