Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b
This work addresses improving answer generation in biomedical QA, but it is incremental as it builds on past methods with a focus on classification vs. regression.
The researchers tackled biomedical question answering by applying query-based multi-document extractive summarization, finding that classification approaches outperformed regression methods in generating multi-sentence answers.
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.