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

• URL: http://arxiv.org/abs/2211.10247v1
• Date: Fri, 18 Nov 2022 14:07:29 GMT
• Title: GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
• Authors: Junyi Bian, Xiaodi Huang, Hong Zhou, Shanfeng Zhu
• Abstract summary: We propose GoSum, a 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.
• Score: 6.4805900740861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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