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arXiv · 2110.06388

HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization

Ye Liu·Jian-Guo Zhang·Yao Wan·Congying Xia·Lifang He·Philip S. Yu
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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.

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Ye Liu, Jian-Guo Zhang, Yao Wan, Congying Xia, Lifang He, Philip S. Yu. 2021-10-12. HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization. https://arxiv.org/abs/2110.06388

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