Improving factual consistency in abstractive summarization has been a focus
of current research. One promising approach is the post-editing method.
However, previous works have yet to make sufficient use of factual factors in
summaries and suffers from the negative effect of the training datasets. In
this paper, we first propose a novel factual error correction model FactCloze
based on a conditional-generation cloze task. FactCloze can construct the
causality among factual factors while being able to determine whether the blank
can be answered or not. Then, we propose a data distillation method to generate
a more faithful summarization dataset SummDSC via multiple-dimensional
evaluation. We experimentally validate the effectiveness of our approach, which
leads to an improvement in multiple factual consistency metrics compared to
baselines.

提出一种基于条件生成填空任务的新型事实错误修正模型 FactCloze，通过多维度评估生成更忠实的摘要数据集 SummDSC，实验证实了该方法的有效性，相比基线方法，在多个事实一致性指标上有所改进。