EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings

With the frequent occurrence of extreme natural phenomena, news about meteorological disasters has increased. As a timely and effective social sensor, social networks have gradually become an important data source for the perception of extreme meteorological events. Meteorological briefing refers to screening valuable knowledge from massive data to provide decision-makers with efficient situational awareness support. However, social network-based briefing content has challenges, including colloquialisms and informal text styles. How to optimize these data in a formal text style is of great significance to improve decision-making efficiency. This paper proposes a meteorological briefing formalization module composed of three models: the text form judgment model, the formalization words detection model, and the event knowledge guided text formalization (EKGTF) model. These models are concatenated to optimize the meteorological briefing, specifically formalizing the briefing content's language style based on Sina Weibo data. As a knowledge-enhanced model, the EKGTF model focuses on describing the core meteorological event knowledge while formalizing the content. Compared to baseline models, the EKGTF model achieves the best results on the BLEU score. Based on the meteorological briefing formalization module, a meteorological briefing formalization service framework is constructed, which is to be applied to the China Meteorological Administration (CMA) Public Meteorological Service Center.