Identifying the relationship between two articles, e.g., whether two articles
published from different sources describe the same breaking news, is critical
to many document understanding tasks. Existing approaches for modeling and
matching sentence pairs do not perform well in matching longer documents, which
embody more complex interactions between the enclosed entities than a sentence
does. To model article pairs, we propose the Concept Interaction Graph to
represent an article as a graph of concepts. We then match a pair of articles
by comparing the sentences that enclose the same concept vertex through a
series of encoding techniques, and aggregate the matching signals through a
graph convolutional network. To facilitate the evaluation of long article
matching, we have created two datasets, each consisting of about 30K pairs of
breaking news articles covering diverse topics in the open domain. Extensive
evaluations of the proposed methods on the two datasets demonstrate significant
improvements over a wide range of state-of-the-art methods for natural language
matching.

该论文基于 “概念交互图” 模型，通过一系列编码技术比较包含相同概念顶点的句子，并通过图卷积网络聚合匹配信号来匹配文章。在两个数据集上进行的广泛评估表明，该方法在自然语言匹配方面显著优于众多最先进的方法。