# GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state

**Authors:** Junyi Bian, Xiaodi Huang, Hong Zhou, Shanfeng Zhu  
**arXiv:** [2211.10247](https://arxiv.org/abs/2211.10247)  
**Date:** 2022-11-18  
**Summary page:** https://www.summarizepaper.com/fr/paper/2211.10247v2/

## Abstract

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.