Automatic generation algorithm of power report summary based on machine learning

This study proposes a novel summary generation algorithm that combines TF-IDF, BERT, and Seq2Seq models and aims to automate summary generation of professional reports in the power industry. Firstly, the TF-IDF algorithm is used to identify key terms and topics in power reports to extract the most representative and informative content segments, and then these segments are deeply semantic analyzed by BERT model to enhance the understanding of expertise in the power domain. Finally, the BERT-encoded vector sequence is input into the Seq2Seq framework to generate a coherent and content-rich summary. This combined algorithm takes advantage of complementary advantages, covers the whole process from keyword extraction to semantic understanding to summary generation, and can deal with complex domain knowledge to ensure the professionalism and accuracy of the summary.