  Extracting summaries from long documents can be regarded as sentence
classification using the structural information of the documents. How to use
such structural information to summarize a document is challenging. In this
paper, we propose GoSum, a novel graph and reinforcement learning based
extractive model for long-paper summarization. In particular, GoSum encodes
sentence states in reinforcement learning by building a heterogeneous graph for
each input document at different discourse levels. An edge in the graph
reflects the discourse hierarchy of a document for restraining the semantic
drifts across section boundaries. We evaluate GoSum on two datasets of
scientific articles summarization: PubMed and arXiv. The experimental results
have demonstrated that GoSum achieve state-of-the-art results compared with
strong baselines of both extractive and abstractive models. The ablation
studies further validate that the performance of our GoSum benefits from the
use of discourse information.
