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To capture the semantic graph structure from raw text, most existingsummarization approaches are built on GNNs with a pre-trained model. However,these methods suffer from cumbersome procedures and inefficient computationsfor long-text documents. To mitigate these issues, this paper proposesHETFORMER, a Transformer-based pre-trained model with multi-granularity sparseattentions for long-text extractive summarization. Specifically, we modeldifferent types of semantic nodes in raw text as a potential heterogeneousgraph and directly learn heterogeneous relationships (edges) among nodes byTransformer. Extensive experiments on both single- and multi-documentsummarization tasks show that HETFORMER achieves state-of-the-art performancein Rouge F1 while using less memory and fewer parameters.

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