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

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

With the development of graph convolutional networks (GCN), deep learning
methods have started to be used on graph data. In additional to convolutional
layers, pooling layers are another important components of deep learning.
However, no effective pooling methods have been developed for graphs currently.
In this work, we propose the graph pooling (gPool) layer, which employs a
trainable projection vector to measure the importance of nodes in graphs. By
selecting the k-most important nodes to form the new graph, gPool achieves the
same objective as regular max pooling layers operating on images. Another
limitation of GCN when used on graph-based text representation tasks is that,
GCNs do not consider the order information of nodes in graph. To address this
limitation, we propose the hybrid convolutional (hConv) layer that combines GCN
and regular convolutional operations. The hConv layer is capable of increasing
receptive fields quickly and computing features automatically. Based on the
proposed gPool and hConv layers, we develop new deep networks for text
categorization tasks. Our results show that the networks based on gPool and
hConv layers achieves new state-of-the-art performance as compared to baseline
methods.

提出了使用可训练投影向量评估节点重要性来选择新图形的最重要的 K 个节点以达到与图像最大池化层相同目的的图形池 (pooling) 层。同时，结合 GCN 和普通卷积运算进行的混合卷积层 (hConv) 能够快速增加接受野 (receptive field)，自动计算特征等功能，基于提出的 gPool 和 hConv 层，我们为文本分类任务开发了新的深度网络，结果证明基于这些层的网络相比于基线方法具有新的最先进性能。