Key News Event Detection and Event Context Using Graphic Convolution, Clustering, and Summarizing Methods

Abstract

The detection of key events and identification of the events’ context have been widely studied to detect key events from large volumes of online news and identify trends in such events. In this paper, we propose a Key News Event Detection and Context Method based on graphic convolving, clustering, and summarizing methods. Our method has three main contributions: (1) We propose the use of position vectors as time-embedding feature representations and concatenate semantic and time-embedding features as node features of the graph to distinguish different nodes of the graph. Additionally, a temporal nonlinear function was constructed using time embedding to objectively describe the effect of time on the degree of association between nodes. (2) We update the graph nodes using a graph convolutional neural network to extract deep semantic information about individual nodes of a high-quality phrase graph, thereby improving the clustering capability of graph-based key event detection. (3) We apply a summary generation algorithm to a subset of news data for each key event. Lastly, we validated the effectiveness of our proposed method by applying it to the 2014 Ebola dataset. The experimental results indicate that our proposed method can effectively detect key events from news documents with high precision and completeness while naturally generating the event context of key events, as compared to EvMine and other existing methods.