State-of-the-art abstractive summarization systems frequently hallucinate content that is not supported by the source document, mainly due to noise in the training dataset. Existing methods opt to drop the noisy samples or tokens from the training set entirely, reducing the effective training set size and creating an artificial propensity to copy words from the source. In this work, we propose a training objective for abstractive summarization based on rejection learning, in which the model learns whether or not to reject potentially noisy tokens. We further propose a regularized decoding objective that penalizes non-factual candidate summaries during inference by using the rejection probability learned during training. We show that our method considerably improves the factuality of generated summaries in automatic and human evaluations when compared to five baseline models and that it does so while increasing the abstractiveness of the generated summaries.

翻译：最先进的抽象摘要系统经常生成源文档中不支持的幻觉内容，这主要源于训练数据集的噪声。现有方法倾向于完全丢弃训练集中的噪声样本或词汇，从而减少了有效训练集规模，并人为地导致模型倾向于从源文本中复制词汇。本文提出一种基于拒绝学习的抽象摘要训练目标，使模型能够学习是否应拒绝潜在的噪声词汇。我们进一步提出一种正则化解码目标，通过在推理阶段利用训练时习得的拒绝概率惩罚非事实性候选摘要。实验表明，与五种基线模型相比，所提方法在自动评估和人工评估中显著提升生成摘要的事实准确性，同时增强了摘要的抽象性。