Despite substantial progress in abstractive text summarization to generate
fluent and informative texts, the factual inconsistency in the generated
summaries remains an important yet challenging problem to be solved. In this
paper, we construct causal graphs for abstractive text summarization and
identify the intrinsic causes of the factual inconsistency, i.e., the language
bias and irrelevancy bias, and further propose a debiasing framework, named
CoFactSum, to alleviate the causal effects of these biases by counterfactual
estimation. Specifically, the proposed CoFactSum provides two counterfactual
estimation strategies, i.e., Explicit Counterfactual Masking with an explicit
dynamic masking strategy, and Implicit Counterfactual Training with an implicit
discriminative cross-attention mechanism. Meanwhile, we design a Debiasing
Degree Adjustment mechanism to dynamically adapt the debiasing degree at each
decoding step. Extensive experiments on two widely-used summarization datasets
demonstrate the effectiveness of CoFactSum in enhancing the factual consistency
of generated summaries compared with several baselines.

本研究提出了一种名为 CoFactSum 的去偏见框架，通过反事实估计，减轻了生成摘要时的语言偏见和不相关偏见的影响，并使用动态的偏差度调整机制。实验结果表明，与几种基线方法相比，CoFactSum 在提高生成的摘要的事实一致性方面具有显著效果。