HEGEL: Hypergraph Transformer for Long Document Summarization
Haopeng Zhang, Xiao Liu, Jiawei Zhang
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.
BibTeX
@inproceedings{zhang-etal-2022-hegel,
title = "{HEGEL}: Hypergraph Transformer for Long Document Summarization",
author = "Zhang, Haopeng and
Liu, Xiao and
Zhang, Jiawei",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.692/",
doi = "10.18653/v1/2022.emnlp-main.692",
pages = "10167--10176"
}