  Text classification is a critical research topic with broad applications in
natural language processing. Recently, graph neural networks (GNNs) have
received increasing attention in the research community and demonstrated their
promising results on this canonical task. Despite the success, their
performance could be largely jeopardized in practice since they are: (1) unable
to capture high-order interaction between words; (2) inefficient to handle
large datasets and new documents. To address those issues, in this paper, we
propose a principled model -- hypergraph attention networks (HyperGAT), which
can obtain more expressive power with less computational consumption for text
representation learning. Extensive experiments on various benchmark datasets
demonstrate the efficacy of the proposed approach on the text classification
task.
