| Citation: | HUANG H N，CHEN Z M，XU C，et al. Automatic summarization model of aerospace news based on domain concept graph[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（1）：317-327 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0233
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The effectiveness of subsequent intelligence analysis can be increased by comprehending and compressing the vast amount of aerospace information that is hidden in the Internet's aerospace news. However the general automatic summarization algorithms tend to ignore many domain key Information, and the existing supervised automatic summarization algorithms need to annotate a lot of data in the domain text. It is time-consuming and laborious. Therefore, we proposed an unsupervised automatic summarization model TextRank based on domain concept graph (DCG-TextRank). It is based on a domain concept graph, which uses domain terms to help guide graph ordering and improve the model's understanding of domain text. The model has three modules: domain concept graph generation, graph weight initialization, graph sorting and semantic filtering. Transform the text into domain concept graph containing sentence nodes and domain term nodes according to sentence vector similarity and domain term database. Initialize the domain concept graph weight according to the features of aerospace news text. Use the TextRank algorithm to sort the sentences, and in the semantic filtering module, the output of TextRank is improved by clustering the graph nodes and setting the semantic retention of the abstract, which fully preserves the semantic Information of text and reduces redundancy. The proposed model is domain portable, and experimental findings indicate that in the aerospace news dataset, the proposed model performs 14.97% better than the conventional TextRank model and 4.37%～12.97% better than the supervised extraction text summary models BertSum and MatchSum.
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