AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization

Alexander Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, Mona Diab


Abstract
Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. Each question thread can receive a large number of answers with different perspectives. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. A major obstacle for this task is the absence of a dataset to provide supervision for producing such summaries. Recent works propose heuristics to create such data, but these are often noisy and do not cover all answer perspectives present. This work introduces a novel dataset of 4,631 CQA threads for answer summarization curated by professional linguists. Our pipeline gathers annotations for all subtasks of answer summarization, including relevant answer sentence selection, grouping these sentences based on perspectives, summarizing each perspective, and producing an overall summary. We analyze and benchmark state-of-the-art models on these subtasks and introduce a novel unsupervised approach for multi-perspective data augmentation that boosts summarization performance according to automatic evaluation. Finally, we propose reinforcement learning rewards to improve factual consistency and answer coverage and analyze areas for improvement.
Anthology ID:
2022.naacl-main.180
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2508–2520
Language:
URL:
https://aclanthology.org/2022.naacl-main.180
DOI:
10.18653/v1/2022.naacl-main.180
Bibkey:
Cite (ACL):
Alexander Fabbri, Xiaojian Wu, Srini Iyer, Haoran Li, and Mona Diab. 2022. AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2508–2520, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization (Fabbri et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.180.pdf
Code
 alex-fabbri/answersumm
Data
AnswerSummCNN/Daily MailCQASUMM