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 pa-per's approach by 2.18％increase in NR@1 metric compared with the baseline.
维普
万方数据