@inproceedings{mrini-etal-2021-joint,
title = "Joint Summarization-Entailment Optimization for Consumer Health Question Understanding",
author = "Mrini, Khalil and
Dernoncourt, Franck and
Chang, Walter and
Farcas, Emilia and
Nakashole, Ndapa",
editor = "Shivade, Chaitanya and
Gangadharaiah, Rashmi and
Gella, Spandana and
Konam, Sandeep and
Yuan, Shaoqing and
Zhang, Yi and
Bhatia, Parminder and
Wallace, Byron",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlpmc-1.8",
doi = "10.18653/v1/2021.nlpmc-1.8",
pages = "58--65",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Summarization-Entailment Optimization for Consumer Health Question Understanding
%A Mrini, Khalil
%A Dernoncourt, Franck
%A Chang, Walter
%A Farcas, Emilia
%A Nakashole, Ndapa
%Y Shivade, Chaitanya
%Y Gangadharaiah, Rashmi
%Y Gella, Spandana
%Y Konam, Sandeep
%Y Yuan, Shaoqing
%Y Zhang, Yi
%Y Bhatia, Parminder
%Y Wallace, Byron
%S Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F mrini-etal-2021-joint
%X 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.
%R 10.18653/v1/2021.nlpmc-1.8
%U https://aclanthology.org/2021.nlpmc-1.8
%U https://doi.org/10.18653/v1/2021.nlpmc-1.8
%P 58-65
Markdown (Informal)
[Joint Summarization-Entailment Optimization for Consumer Health Question Understanding](https://aclanthology.org/2021.nlpmc-1.8) (Mrini et al., NLPMC 2021)
ACL