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：主要由于培训数据集中的噪音,现有方法选择完全从培训组完全放弃吵闹的样本或标牌,缩小有效的培训组规模,人为地制造一种复制源词的倾向。在这项工作中,我们提出了一个基于拒绝学习的抽象总结培训目标,模型在其中学习是否拒绝可能吵闹的标语。我们进一步提议一个常规化的解码目标,在推断过程中利用在培训中学会的拒绝概率来惩罚非事实候选人摘要。我们表明,我们的方法大大改进了自动和人类评价中生成的摘要与五个基线模型相比的真实性,同时提高了生成摘要的抽象性。