Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback
Artificial Intelligence
2022-05-13 v2
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.
Cite
@article{arxiv.2204.05512,
title = {Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback},
author = {Duy-Hung Nguyen and Nguyen Viet Dung Nghiem and Bao-Sinh Nguyen and Dung Tien Le and Shahab Sabahi and Minh-Tien Nguyen and Hung Le},
journal= {arXiv preprint arXiv:2204.05512},
year = {2022}
}
Comments
The paper is accepted at NAACL 2022