  Neural abstractive summarization models have led to promising results in
summarizing relatively short documents. We propose the first model for
abstractive summarization of single, longer-form documents (e.g., research
papers). Our approach consists of a new hierarchical encoder that models the
discourse structure of a document, and an attentive discourse-aware decoder to
generate the summary. Empirical results on two large-scale datasets of
scientific papers show that our model significantly outperforms
state-of-the-art models.
