Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Sep 2019.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 2.
4 papers.
- 2021Biomedical Question Answering: A Survey of Approaches and Challengescited by 46
- 2021Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answeringcited by 13
- 2023Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Trainingcited by 1
- 2023Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learningcited by 1
- 2023Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning29 Nov 2023
- 2023Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training10 Jul 2023
- 2021Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering28 Jun 2021
- 2021Biomedical Question Answering: A Survey of Approaches and Challenges10 Feb 2021