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

18 Nov 2022  ·  Junyi Bian, Xiaodi Huang, Hong Zhou, Shanfeng Zhu ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Text Summarization Pubmed GoSum (extractive) ROUGE-1 49.83 # 4
ROUGE-2 23.56 # 3
ROUGE-L 45.10 # 3


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