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

2022-11-18 14:07:29
Junyi Bian, Xiaodi Huang, Hong Zhou, Shanfeng Zhu
arXiv_CL
         
Abstract

Handling long texts with structural information and excluding redundancy between summary sentences are essential in extractive document summarization. In this work, we propose GoSum, a novel reinforcement-learning-based extractive model for long-paper summarization. GoSum encodes states by building a heterogeneous graph from different discourse levels for each input document. We evaluate the model on two datasets of scientific articles summarization: PubMed and arXiv where it outperforms all extractive summarization models and most of the strong abstractive baselines.

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URL

https://arxiv.org/abs/2211.10247

PDF

https://arxiv.org/pdf/2211.10247.pdf

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