HEGEL: Hypergraph Transformer for Long Document Summarization

• URL: http://arxiv.org/abs/2210.04126v1
• Date: Sun, 9 Oct 2022 00:32:50 GMT
• Title: HEGEL: Hypergraph Transformer for Long Document Summarization
• Authors: Haopeng Zhang, Xiao Liu, Jiawei Zhang
• Abstract summary: This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence relations. We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL.
• Score: 14.930704950433324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extractive summarization for long documents is challenging due to the extended structured input context. The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization. This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order cross-sentence relations. HEGEL updates and learns effective sentence representations with hypergraph transformer layers and fuses different types of sentence dependencies, including latent topics, keywords coreference, and section structure. We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL.

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