Graph Convolutional Networks (GCNs) have shown strong performance in learning
text representations for various tasks such as text classification, due to its
expressive power in modeling graph structure data (e.g., a literature citation
network). Most existing GCNs are limited to deal with documents included in a
pre-defined graph, i.e., it cannot be generalized to out-of-graph documents. To
address this issue, we propose to transform the document graph into a word
graph, to decouple data samples (i.e., documents in training and test sets) and
a GCN model by using a document-independent graph. Such word-level GCN could
therefore naturally inference out-of-graph documents in an inductive way. The
proposed Word-level Graph (WGraph) can not only implicitly learning word
presentation with commonly-used word co-occurrences in corpora, but also
incorporate extra global semantic dependency derived from inter-document
relationships (e.g., literature citations). An inductive Word-grounded Graph
Convolutional Network (WGCN) is proposed to learn word and document
representations based on WGraph in a supervised manner. Experiments on text
classification with and without citation networks evidence that the proposed
WGCN model outperforms existing methods in terms of effectiveness and
efficiency.

提出了一种基于词图的图卷积神经网络模型，能够以归纳的方式完成对于预定义图之外的文档的分类任务，该模型利用词共现信息形成词图，同时融入了文献引用关系的全局语义依赖信息，通过实验验证，该模型在分类任务中表现出良好的性能和高效率。

基于词汇的图卷积网络

Word Grounded Graph Convolutional Network

Radiology reports play a critical role in communicating medical findings to
physicians. In each report, the impression section summarizes essential
radiology findings. In clinical practice, writing impression is highly demanded
yet time-consuming and prone to errors for radiologists. Therefore, automatic
impression generation has emerged as an attractive research direction to
facilitate such clinical practice. Existing studies mainly focused on
introducing salient word information to the general text summarization
framework to guide the selection of the key content in radiology findings.
However, for this task, a model needs not only capture the important words in
findings but also accurately describe their relations so as to generate
high-quality impressions. In this paper, we propose a novel method for
automatic impression generation, where a word graph is constructed from the
findings to record the critical words and their relations, then a Word Graph
guided Summarization model (WGSum) is designed to generate impressions with the
help of the word graph. Experimental results on two datasets, OpenI and
MIMIC-CXR, confirm the validity and effectiveness of our proposed approach,
where the state-of-the-art results are achieved on both datasets. Further
experiments are also conducted to analyze the impact of different graph designs
to the performance of our method.

本文提出了一种新颖的自动印象生成方法，其中从发现中构建词图以记录关键词及其关系，然后设计了一个词图引导的总结模型 (WGSum) 来生成印象。实验结果表明，我们的方法在两个数据集上取得了最先进的结果，这进一步证实了我们的方法的有效性和可行性。