HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization
Ye Liu, Jianguo Zhang, Yao Wan, Congying Xia, Lifang He, Philip Yu
Abstract
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model. However, these methods suffer from cumbersome procedures and inefficient computations for long-text documents. To mitigate these issues, this paper proposes HetFormer, a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. Specifically, we model different types of semantic nodes in raw text as a potential heterogeneous graph and directly learn heterogeneous relationships (edges) among nodes by Transformer. Extensive experiments on both single- and multi-document summarization tasks show that HetFormer achieves state-of-the-art performance in Rouge F1 while using less memory and fewer parameters.
BibTeX
@inproceedings{liu-etal-2021-hetformer,
title = "{HETFORMER}: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization",
author = "Liu, Ye and
Zhang, Jianguo and
Wan, Yao and
Xia, Congying and
He, Lifang and
Yu, Philip",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.13/",
doi = "10.18653/v1/2021.emnlp-main.13",
pages = "146--154"
}