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arXiv · 2204.05512

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 Le
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Abstract

For summarization, human preference 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 human and AI agent 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.

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Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Le, Shahab Sabahi, Minh-Tien Nguyen, Hung Le. 2022-04-12. Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback. https://arxiv.org/abs/2204.05512

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