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.

通过引入结构因果模型 (SCM) 从而诱导总结数据的潜在因果结构，我们提出了一种因果启发式序列到序列模型 (CI-Seq2Seq) 来学习可以模拟因果因素的因果表示，以引导我们获取总结生成的因果信息。在两个广泛使用的文本总结数据集上的实验结果证明了我们方法的优势。

诱导抽象文本摘要的因果结构

Inducing Causal Structure for Abstractive Text Summarization

This paper proposes a new eXplanation framework, called OrphicX, for
generating causal explanations for any graph neural networks (GNNs) based on
learned latent causal factors. Specifically, we construct a distinct generative
model and design an objective function that encourages the generative model to
produce causal, compact, and faithful explanations. This is achieved by
isolating the causal factors in the latent space of graphs by maximizing the
information flow measurements. We theoretically analyze the cause-effect
relationships in the proposed causal graph, identify node attributes as
confounders between graphs and GNN predictions, and circumvent such confounder
effect by leveraging the backdoor adjustment formula. Our framework is
compatible with any GNNs, and it does not require access to the process by
which the target GNN produces its predictions. In addition, it does not rely on
the linear-independence assumption of the explained features, nor require prior
knowledge on the graph learning tasks. We show a proof-of-concept of OrphicX on
canonical classification problems on graph data. In particular, we analyze the
explanatory subgraphs obtained from explanations for molecular graphs (i.e.,
Mutag) and quantitatively evaluate the explanation performance with frequently
occurring subgraph patterns. Empirically, we show that OrphicX can effectively
identify the causal semantics for generating causal explanations, significantly
outperforming its alternatives.

提出一种名为 OrphicX 的新的解释框架，用于基于学习的潜在因果因素为任何图神经网络（GNN）生成因果解释，该框架与任何 GNN 兼容，不需要访问目标 GNN 生成其预测的过程，无需先验知识，并证明了其优于其他解释方法。