LCHQA-Summ: Multi-perspective Summarization of Publicly Sourced Consumer Health Answers

Abari Bhattacharya, Rochana Chaturvedi, Shweta Yadav


Abstract
Community question answering forums provide a convenient platform for people to source answers to their questions including those related to healthcare from the general public. The answers to user queries are generally long and contain multiple different perspectives, redundancy or irrelevant answers. This presents a novel challenge for domain-specific concise and correct multi-answer summarization which we propose in this paper.
Anthology ID:
2022.nlg4health-1.3
Volume:
Proceedings of the First Workshop on Natural Language Generation in Healthcare
Month:
July
Year:
2022
Address:
Waterville, Maine, USA and virtual meeting
Editors:
Emiel Krahmer, Kathy McCoy, Ehud Reiter
Venue:
NLG4Health
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
23–26
Language:
URL:
https://aclanthology.org/2022.nlg4health-1.3
DOI:
Bibkey:
Cite (ACL):
Abari Bhattacharya, Rochana Chaturvedi, and Shweta Yadav. 2022. LCHQA-Summ: Multi-perspective Summarization of Publicly Sourced Consumer Health Answers. In Proceedings of the First Workshop on Natural Language Generation in Healthcare, pages 23–26, Waterville, Maine, USA and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
LCHQA-Summ: Multi-perspective Summarization of Publicly Sourced Consumer Health Answers (Bhattacharya et al., NLG4Health 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.nlg4health-1.3.pdf