The mainstream of data-driven abstractive summarization models tends to explore the correlations rather than the causal relationships. Among such correlations, there can be spurious ones which suffer from the language prior learned from the training corpus and therefore undermine the overall effectiveness of the learned model. To tackle this issue, we introduce a Structural Causal Model (SCM) to induce the underlying causal structure of the summarization data. We assume several latent causal factors and non-causal factors, representing the content and style of the document and summary. Theoretically, we prove that the latent factors in our SCM can be identified by fitting the observed training data under certain conditions. On the basis of this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq) to learn the causal representations that can mimic the causal factors, guiding us to pursue causal information for summary generation. The key idea is to reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of the document and summary variables from the training corpus. Experimental results on two widely used text summarization datasets demonstrate the advantages of our approach.

翻译：数据驱动的抽象式摘要模型主流倾向于探索相关性而非因果关系。在这些相关性中，可能存在虚假相关性，这些虚假相关性受到从训练语料中学习到的语言先验的影响，从而削弱了所学模型的整体有效性。为了解决这个问题，我们引入了一个结构因果模型（SCM）来诱导摘要数据的潜在因果结构。我们假设存在多个潜在因果因素和非因果因素，分别代表文档和摘要的内容与风格。理论上，我们证明了我们的SCM中的潜在因素可以通过在特定条件下拟合观察到的训练数据来进行识别。在此基础上，我们提出了一种因果启发的序列到序列模型（CI-Seq2Seq），用于学习能够模拟因果因素的因果表示，从而指导我们为摘要生成获取因果信息。关键思路是重新构造变分自编码器（VAE），以拟合训练语料中文档和摘要变量的联合分布。在两个广泛使用的文本摘要数据集上的实验结果表明了我们方法的优势。