This paper presents Macquarie University's participation to the two most
recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B
(BioASQ10b), Phase~B. In these tasks, participating systems are expected to
generate complex answers to biomedical questions, where the answers may contain
more than one sentence. We apply query-focused extractive summarisation
techniques. In particular, we follow a sentence classification-based approach
that scores each candidate sentence associated to a question, and the $n$
highest-scoring sentences are returned as the answer. The Synergy Task
corresponds to an end-to-end system that requires document selection, snippet
selection, 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 by
using a DistilBERT/ALBERT classifier that had been trained on the training data
of BioASQ9b. Document retrieval was achieved as a standard search over the
CORD-19 data using the search API provided by the BioASQ organisers, and
snippet retrieval was achieved by re-ranking the sentences of the top retrieved
documents, 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 biomedical
questions. For this task, we followed a data-centric approach. We hypothesised
that the training data of the first BioASQ years might be biased and we
experimented with different subsets of the training data. We observed an
improvement of results when the system was trained on the second half of the
BioASQ10b training data.

本文介绍了麦考瑞大学参加 BioASQ Synergy Tasks 和 BioASQ10b Task B 的情况，其中采用了查询导向的摘要技术，并使用了 DistilBERT/ALBERT 分类器、文档检索和片段检索的方式，sBERT 向量表示优于 tf.idf，并进行了数据中心的方法实验。