Volume 44 Issue 1
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LUO Guanting, ZOU Yenan, CAI Yanxia. Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model. Chinese Journal of Space Science, 2024, 44(1): 80-94 doi: 10.11728/cjss2024.01.2023-0029
Citation: LUO Guanting, ZOU Yenan, CAI Yanxia. Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model. Chinese Journal of Space Science, 2024, 44(1): 80-94 doi: 10.11728/cjss2024.01.2023-0029
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Automatic Generation of Artificial Space Weather Forecast Product Based on Sequence-to-sequence Model

doi: 10.11728/cjss2024.01.2023-0029 cstr: 32142.14.cjss2024.01.2023-0029
  • 1.

    State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049

  • 3.

    Key Laboratory of Science and Technology on Environment Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190

Funds:  Supported by the Key Research Program of the Chinese Academy of Sciences (ZDRE-KT-2021-3)
  • Received Date: 2023-02-22
  • Rev Recd Date: 2023-04-19
    • Available Online: 2023-07-27
    Abstract
  • 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 language generation methods based on the sequence-to-sequence model, space weather forecast texts can be automatically 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 network sub-models (one Bidirectional Auto-Regressive Transformers pre-trained model and two Transformer models). 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 generating 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 forecasters in generating high-quality space weather forecast products, despite the data being starved.

     

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