论文标题
以查询为集中的生物医学文本的多文章摘要
Query Focused Multi-document Summarisation of Biomedical Texts
论文作者
论文摘要
本文介绍了麦格理大学和2020年Bioasq挑战赛（BioASQ8B）任务B阶段B期的参与。我们的整体框架通过将分类或回归层应用于候选句子嵌入以及问题和句子嵌入之间的比较来实现以查询为重点的多文件提取性摘要。我们使用Bert和Biobert，Siamese架构以及增强学习的变体实验。当使用BERT获取单词嵌入时，我们会观察到最佳结果，然后是LSTM层以获取句子嵌入。使用暹罗体系结构或生物Biobert的变体并不能改善结果。
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.
