<p>This study applies text summarization techniques to scientific and technological texts. This enables precise matching of relevant information, which holds significant importance in the handling and practical use of technological data. However, existing models lack specificity and effectiveness in handling text summarization tasks for scientific and technological information, particularly in their ability to extract semantic features from complex texts. Therefore, this study introduces an automatic summarization model for scientific and technological texts based on the Pegasus-CopyNet framework, the framework consists of three key components. Firstly, this paper designs a multi-dimensional sentence masking strategy to achieve the fine-tuning of the Pegasus model on specific domain datasets, enabling it to generate word embedding vectors enriched with contextual semantic information. Secondly, these word embeddings are used as input to the CopyNet model, where an enhanced CNN module performs local feature extraction. Finally, a technological terminology vocabulary and an optimized vocabulary selection mechanism are integrated, making the model’s summaries in scientific and technological domains more professional and accurate. It can be concluded from the analysis of each experimental result, compared to baseline models, the ROUGE scores in the field of scientific and technological information long-text summarization were improved in this paper, reaching 41.62% (ROUGE-1), 22.06% (ROUGE-2), and 36.41% (ROUGE-L).</p>