  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.
