Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b

Abstract

Task B Phase B of the 2019 BioASQ challenge focuses on biomedical question answering. Macquarie University's participation applies query-based multi-document extractive summarisation techniques to generate a multi-sentence answer given the question and the set of relevant snippets. In past participation we explored the use of regression approaches using deep learning architectures and a simple policy gradient architecture. For the 2019 challenge we experiment with the use of classification approaches with and without reinforcement learning. In addition, we conduct a correlation analysis between various ROUGE metrics and the BioASQ human evaluation scores.

arXiv comment12 pages, 3 figures, 7 tables. As accepted at BioASQ workshop, ECML-PKDD 2019

References in corpus (3)

• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
• Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
• Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation

Cited by in corpus (4)

• Biomedical Question Answering: A Survey of Approaches and Challenges
• Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
• Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
• Query Focused Multi-document Summarisation of Biomedical Texts