  Causal explanation analysis (CEA) can assist us to understand the reasons
behind daily events, which has been found very helpful for understanding the
coherence of messages. In this paper, we focus on Causal Explanation Detection,
an important subtask of causal explanation analysis, which determines whether a
causal explanation exists in one message. We design a Pyramid Salient-Aware
Network (PSAN) to detect causal explanations on messages. PSAN can assist in
causal explanation detection via capturing the salient semantics of discourses
contained in their keywords with a bottom graph-based word-level salient
network. Furthermore, PSAN can modify the dominance of discourses via a top
attention-based discourse-level salient network to enhance explanatory
semantics of messages. The experiments on the commonly used dataset of CEA
shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on
the Causal Explanation Detection task.
