  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.
