Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback
Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Tien Le, Shahab Sabahi, Minh-Tien Nguyen, Hung Le
Abstract
For summarization, human preferences is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous. Practical settings require dynamic exchanges between humans and AI agents wherein feedback is provided in an online manner, a few at a time. In this paper, we introduce a new framework to train summarization models with preference feedback interactively. By properly leveraging offline data and a novel reward model, we improve the performance regarding ROUGE scores and sample-efficiency. Our experiments on three various datasets confirm the benefit of the proposed framework in active, few-shot and online settings of preference learning.- Anthology ID:
- 2022.findings-naacl.147
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2022
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Marine Carpuat, Marie-Catherine de Marneffe, Ivan Vladimir Meza Ruiz
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1919–1930
- Language:
- URL:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.findings-naacl.147/
- DOI:
- 10.18653/v1/2022.findings-naacl.147
- Cite (ACL):
- Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Tien Le, Shahab Sabahi, Minh-Tien Nguyen, and Hung Le. 2022. Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 1919–1930, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback (Nguyen et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.findings-naacl.147.pdf
- Data
- BillSum, Reddit TIFU