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