通过对比奖励学习提升抽象式摘要的事实性
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
AI总结：
本文提出一种结合奖励学习与事实性指标的对比学习框架，使抽象式摘要模型从事实性反馈中学习，从而生成更符合事实的摘要。
摘要：
现代抽象式摘要模型常常生成包含幻觉或矛盾信息的摘要。本文提出了一种简单而有效的对比学习框架，该框架结合了奖励学习和事实性指标的最新进展。实证研究表明，所提出的框架使摘要模型能够通过对比奖励学习从事实性指标的反馈中学习，从而在人类评估中生成更具事实性的摘要。这表明学习和评估算法的进一步进步可以直接用于提供更具事实性的摘要。
英文摘要：
Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
