Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence ModelOA北大核心CSTPCD

Both analyzing a large amount of space weather observed data and alleviating personal experience bias are significant challenges in generating artificial space weather forecast products.With the use of natural lan-guage generation methods based on the sequence-to-sequence model,space weather forecast texts can be automati-cally generated.To conduct our generation tasks at a fine-grained level,a taxonomy of space weather phenomena based on descriptions is presented.Then,our MDH(Multi-Domain Hybrid)model is proposed for generating space weather summaries in two stages.This model is composed of three sequence-to-sequence-based deep neural net-work sub-models(one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer mo-dels).Then,to evaluate how well MDH performs,quality evaluation metrics based on two prevalent automatic metrics and our innovative human metric are presented.The comprehensive scores of the three summaries generat-ing tasks on testing datasets are 70.87,93.50,and 92.69,respectively.The results suggest that MDH can generate space weather summaries with high accuracy and coherence,as well as suitable length,which can assist forecast-ers in generating high-quality space weather forecast products,despite the data being starved.