Matching Article Pairs with Graphical Decomposition and Convolutions
Bang Liu†, Di Niu†, Haojie Wei‡, Jinghong Lin‡, Yancheng He‡, Kunfeng Lai‡, Yu Xu‡ †University of Alberta, Edmonton, AB, Canada {bang3, ‡Platform and Content Group, Tencent, Shenzhen, China {fayewei, daphnelin, collinhe, calvinlai,
Abstract
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.
中文速览
新闻爆发时，同一事件往往被不同媒体用各自的措辞写成大量文章，如何自动判断两篇长文章是否在报道同一条新闻，是新闻聚合与去重的核心难题。现有文本匹配方法主要为句子对设计，面对结构复杂、实体交织的长文章时力不从心。为此，研究者提出"概念交互图"（Concept Interaction Graph，CIG）方法：先用关键词提取和社区检测把每篇文章转化为一张以概念为节点的加权图，再对两篇文章中共享的概念节点分别提取句子集合并做局部语义匹配，最后用图卷积网络（Graph Convolutional Network，GCN）综合所有节点的局部匹配信号，输出整体匹配结果。为支撑评测，团队还构建了两个各含约3万篇中文新闻文章对的标注数据集，覆盖开放域多元话题。实验显示，与多种主流文本匹配方法相比，该方法在两个数据集上的分类准确率分别提升了17.31%和23.09%，证明基于图结构的"分而治之"思路能显著提升长文档匹配的效果，对新闻去重、事件聚合等实际场景具有重要价值。
原文 arXiv:1802.07459;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1802.07459v2