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    <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.</abstract>
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%0 Conference Proceedings
%T HEGEL: Hypergraph Transformer for Long Document Summarization
%A Zhang, Haopeng
%A Liu, Xiao
%A Zhang, Jiawei
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zhang-etal-2022-hegel
%X 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.
%R 10.18653/v1/2022.emnlp-main.692
%U https://aclanthology.org/2022.emnlp-main.692
%U https://doi.org/10.18653/v1/2022.emnlp-main.692
%P 10167-10176

Markdown (Informal)

[HEGEL: Hypergraph Transformer for Long Document Summarization](https://aclanthology.org/2022.emnlp-main.692) (Zhang et al., EMNLP 2022)

• HEGEL: Hypergraph Transformer for Long Document Summarization (Zhang et al., EMNLP 2022)

ACL

• Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2022. HEGEL: Hypergraph Transformer for Long Document Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10167–10176, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.