Abstractive summarization models often generate factually inconsistent
content particularly when the parametric knowledge of the model conflicts with
the knowledge in the input document. In this paper, we analyze the robustness
of fine-tuning based summarization models to the knowledge conflict, which we
call factual adaptiveness. We utilize pre-trained language models to construct
evaluation sets and find that factual adaptiveness is not strongly correlated
with factual consistency on original datasets. Furthermore, we introduce a
controllable counterfactual data augmentation method where the degree of
knowledge conflict within the augmented data can be adjustable. Our
experimental results on two pre-trained language models (PEGASUS and BART) and
two fine-tuning datasets (XSum and CNN/DailyMail) demonstrate that our method
enhances factual adaptiveness while achieving factual consistency on original
datasets on par with the contrastive learning baseline.

通过分析对知识冲突的鲁棒性，本文研究了基于微调的抽象概括模型，发现并引入了一种可控的反事实数据增广方法来增强事实适应性，并在两种预训练语言模型（PEGASUS 和 BART）以及两个微调数据集（XSum 和 CNN/DailyMail）上实验证明，该方法在实现原始数据集上的事实一致性的同时提高了事实适应性。