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

本文提出了一种新型的异构词-字符文本图融合文档节点，用于更好地学习它们之间的依赖关系，同时提出了两种基于图的神经模型WCTextGCN和WCTextGAT，实验结果表明，这些模型在文本分类和自动文本摘要任务中表现出了更好的性能。

使用字符和词级 N-Grams 改进基于图形的文本表示