Joint Summarization-Entailment Optimization for Consumer Health Question Understanding

Khalil Mrini, Franck Dernoncourt, Walter Chang, Emilia Farcas, Ndapa Nakashole


Abstract
Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems. We propose a novel data-augmented and simple joint learning approach combining question summarization and Recognizing Question Entailment (RQE) in the medical domain. Our data augmentation approach enables to use just one dataset for joint learning. We show improvements on both tasks across four biomedical datasets in accuracy (+8%), ROUGE-1 (+2.5%) and human evaluation scores. Human evaluation shows joint learning generates faithful and informative summaries. Finally, we release our code, the two question summarization datasets extracted from a large-scale medical dialogue dataset, as well as our augmented datasets.
Anthology ID:
2021.nlpmc-1.8
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | NLPMC
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–65
Language:
URL:
https://aclanthology.org/2021.nlpmc-1.8
DOI:
10.18653/v1/2021.nlpmc-1.8
Bibkey:
Cite (ACL):
Khalil Mrini, Franck Dernoncourt, Walter Chang, Emilia Farcas, and Ndapa Nakashole. 2021. Joint Summarization-Entailment Optimization for Consumer Health Question Understanding. In Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, pages 58–65, Online. Association for Computational Linguistics.
Cite (Informal):
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding (Mrini et al., NLPMC 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nlpmc-1.8.pdf
Code
 khalilmrini/medical-question-understanding
Data
GLUEMeQSum