融入文档图和事件图的新闻核心事件检测  

News Salient Event Detection Incorporating Document Graph and Event Graph

作　　者：赵庆珏 余正涛[1,2] 王剑[1,2] 黄于欣[1,2] 朱恩昌 ZHAO Qingjue;YU Zhengtao;WANG Jian;HUANG Yuxin;ZHU Enchang(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming,Yunnan 650500,China;Yunnan Key Laboratory of Artificial Intelligence,Kunming University of Science and Technology,Kunming,Yunnan 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500 [2]昆明理工大学云南省人工智能重点实验室,云南昆明650500

出　　处：《中文信息学报》2024年第5期99-106,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金(U21B2027,61972186,61732005,61866019);云南省重大科技专项(202002AD080001,202202AD080003,202103AA080015);云南省高新技术产业专项(201606)。

摘　　要：新闻核心事件检测旨在从非结构化的新闻文本中检测出最能代表新闻核心内容的事件。新闻报道的多个事件之间存在着复杂的关联关系,且同一个事件的事件要素分布在不同的句子甚至不同的段落中,传统的方法对事件之间的关联关系以及事件的全局语义信息建模不充分。因此,该文提出了融入文档图和事件图的新闻核心事件检测方法。该方法首先通过构建文档图和事件图来建模新闻文本的全局语义特征和事件之间的关联特征。然后,通过图卷积神经网络捕获高阶邻域信息,获得文档表征和事件表征。最后,将得到的文档表征和事件表征使用交叉注意力进一步捕获事件全局语义信息。在纽约时报数据集上的实验结果验证了该文方法的有效性,NR@1较基线方法提升2.18%。News salient event detection aims to detect the events that best represent the core content of unstructured news texts.There are complex correlations among multiple events in news reports,and the event elements of the same event are distributed in different sentences or even different paragraphs.To deal with this issue,this paper proposes a news salient event detection method that incorporates document graph and event graph.The method first models the news texts global semantic features and the association features between events by constructing document graphs and event graphs.Then,the document representation and event representation are obtained by capturing higher-order neighborhood information through graph convolutional neural networks.Finally,the obtained document representations and event representations are used to further capture the global semantic information of events using cross-attention.Experimental results on the New York Times Annotated Corpus validate the effectiveness of the paper's approach by 2.18%increase in NR@1 metric compared with the baseline.

关 键 词：核心事件检测 文档图 事件图 交叉注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]