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Graph-based text representation focuses on how text documents are representedas graphs for exploiting dependency information between tokens and documentswithin a corpus. Despite the increasing interest in graph representationlearning, there is limited research in exploring new ways for graph-based textrepresentation, which is important in downstream natural language processingtasks. In this paper, we first propose a new heterogeneous word-character textgraph that combines word and character n-gram nodes together with documentnodes, allowing us to better learn dependencies among these entities.Additionally, we propose two new graph-based neural models, WCTextGCN andWCTextGAT, for modeling our proposed text graph. Extensive experiments in textclassification and automatic text summarization benchmarks demonstrate that ourproposed models consistently outperform competitive baselines andstate-of-the-art graph-based models.

Improving Graph-Based Text Representations with Character and Word Level N-grams