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:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.nlg4health-1.3.pdf