News2Mapping: A news events correlation model for news videos
Abstract: News video is an important way of news communication, and people can easily get news from all over the world through the Internet. However, it lacks in the organization of news video content about temporality, presentation and relevance and fails to express the correlation between news events. In this paper, we present an event correlation model of news videos to organize news content. News content is clustered through topics, and the relationship between events is illustrated through relationship mappings. To achieve this purpose, an XLNet-based language model is presented to extract news keywords and their relationships. The clustering algorithm is designed to obtain news event topic clustering and named entity clustering. At the same time, we build the relationship mappings in news events to visualize the correlation between news events better. The user study is also designed to investigate the performance of our method in news reading and understanding. Compared with peer methods, the news information organized by our method achieves a higher user satisfaction level.
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