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 Le, Shahab Sabahi, Minh-Tien Nguyen, Hung Lê · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
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.