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

arXiv: https://arxiv.org/abs/1909.00542 · HTML: https://mlsanity.com/paper/1909.00542/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Biomedical Question Answering: A Survey of Approaches and Challenges](https://mlsanity.com/paper/2102.05281/) (2021)
- [Overview of BioASQ 2020: The eighth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14618/) (2021)
- [Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training](https://mlsanity.com/paper/2307.04412/) (2023)
- [Reinforcement Replaces Supervision: Query focused Summarization using Deep Reinforcement Learning](https://mlsanity.com/paper/2311.17514/) (2023)

## 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.