  Despite the success of attention-based neural models for natural language
generation and classification tasks, they are unable to capture the discourse
structure of larger documents. We hypothesize that explicit discourse
representations have utility for NLP tasks over longer documents or document
sequences, which sequence-to-sequence models are unable to capture. For
abstractive summarization, for instance, conventional neural models simply
match source documents and the summary in a latent space without explicit
representation of text structure or relations. In this paper, we propose to use
neural discourse representations obtained from a rhetorical structure theory
(RST) parser to enhance document representations. Specifically, document
representations are generated for discourse spans, known as the elementary
discourse units (EDUs). We empirically investigate the benefit of the proposed
approach on two different tasks: abstractive summarization and popularity
prediction of online petitions. We find that the proposed approach leads to
improvements in all cases.
