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Previous work indicates that discourse information benefits summarization. Inthis paper, we explore whether this synergy between discourse and summarizationis bidirectional, by inferring document-level discourse trees from pre-trainedneural summarizers. In particular, we generate unlabeled RST-style discoursetrees from the self-attention matrices of the transformer model. Experimentsacross models and datasets reveal that the summarizer learns both, dependency-and constituency-style discourse information, which is typically encoded in asingle head, covering long- and short-distance discourse dependencies. Overall,the experimental results suggest that the learned discourse information isgeneral and transferable inter-domain.

Predicting Discourse Trees from Transformer-based Neural Summarizers