Focus-Driven Contrastive Learning for Medical Question Summarization

Ming Zhang, Shuai Dou, Ziyang Wang, Yunfang Wu


Abstract
Automatic medical question summarization can significantly help the system to understand consumer health questions and retrieve correct answers. The Seq2Seq model based on maximum likelihood estimation (MLE) has been applied in this task, which faces two general problems: the model can not capture well question focus and and the traditional MLE strategy lacks the ability to understand sentence-level semantics. To alleviate these problems, we propose a novel question focus-driven contrastive learning framework (QFCL). Specially, we propose an easy and effective approach to generate hard negative samples based on the question focus, and exploit contrastive learning at both encoder and decoder to obtain better sentence level representations. On three medical benchmark datasets, our proposed model achieves new state-of-the-art results, and obtains a performance gain of 5.33, 12.85 and 3.81 points over the baseline BART model on three datasets respectively. Further human judgement and detailed analysis prove that our QFCL model learns better sentence representations with the ability to distinguish different sentence meanings, and generates high-quality summaries by capturing question focus.
Anthology ID:
2022.coling-1.539
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6176–6186
Language:
URL:
https://aclanthology.org/2022.coling-1.539
DOI:
Bibkey:
Cite (ACL):
Ming Zhang, Shuai Dou, Ziyang Wang, and Yunfang Wu. 2022. Focus-Driven Contrastive Learning for Medical Question Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6176–6186, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Focus-Driven Contrastive Learning for Medical Question Summarization (Zhang et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.539.pdf
Data
MeQSum