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Text classification is a critical research topic with broad applications innatural language processing. Recently, graph neural networks (GNNs) havereceived increasing attention in the research community and demonstrated theirpromising results on this canonical task. Despite the success, theirperformance could be largely jeopardized in practice since they are: (1) unableto capture high-order interaction between words; (2) inefficient to handlelarge datasets and new documents. To address those issues, in this paper, wepropose a principled model -- hypergraph attention networks (HyperGAT), whichcan obtain more expressive power with less computational consumption for textrepresentation learning. Extensive experiments on various benchmark datasetsdemonstrate the efficacy of the proposed approach on the text classificationtask.

Be More with Less: Hypergraph Attention Networks for Inductive Text Classification