# 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/en/paper/2211.10247v2/

## Key points

- Authors: Junyi Bian, Xiaodi Huang, Hong Zhou, Shanfeng Zhu
- Title: "GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized Discourse State"
- Combines graph organization and reinforcement learning techniques
- Encodes sentence states through reinforcement learning
- Constructs a heterogeneous graph for each input document at various discourse levels
- Maintains coherence and prevents semantic drifts across section boundaries
- Tested on PubMed and arXiv datasets for scientific article summarization
- Outperforms existing extractive and abstractive models with state-of-the-art performance
- Importance of leveraging structural cues within documents for improved summarization outcomes

## Summary

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In their paper titled "GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized Discourse State," authors Junyi Bian, Xiaodi Huang, Hong Zhou, and Shanfeng Zhu address the challenge of extracting summaries from long documents by utilizing the structural information present in the text. They introduce GoSum, a novel extractive model that combines graph organization and reinforcement learning techniques to summarize lengthy scientific articles effectively. The model operates by encoding sentence states through reinforcement learning and constructing a heterogeneous graph for each input document at various discourse levels. The edges in this graph represent the discourse hierarchy of the document, maintaining coherence and preventing semantic drifts across section boundaries. GoSum is evaluated on two datasets - PubMed and arXiv - for scientific article summarization, outperforming existing extractive and abstractive models with state-of-the-art performance. The authors also conduct ablation studies to further validate the effectiveness of incorporating discourse information into GoSum. Overall, their research highlights the importance of leveraging structural cues within documents for improved summarization outcomes.