关键词: 文本摘要, 知识蒸馏, 提示工程, 大语言模型

Abstract: Text summary generation is one of the main research directions in the field of natural language processing. In response to the current issues such as weak semantic extraction capabilities of Chinese abstract models, unstable generation quality of large models, and high requirements for deployment resources, a Chinese text summarization generation method incorporating knowledge distillation is proposed. Firstly, a multi-threaded approach is employed to invoke the large model interface for augmenting the training data, with the introduction of prompt engineering to align the summary quality and generate reference labels. Then, with advantages of knowledge distillation in knowledge transfer, offline knowledge distillation is adopted, and the output of large model is used as knowledge to teach the student model, enhancing the accuracy and readability of the student model??s summary, while reducing training costs and resource consumption. Finally, the student model is improved by incorporating a copy mechanism to mitigate the out-of-vocabulary (OOV) word issue, and bidirectional similarity calculations are performed on the output results to optimize the loss function, further enhancing model stability. Experiments on the NLPCC2017 dataset demonstrate that the proposed method outperforms existing mainstream summarization generation methods in terms of comprehensive performance on the ROUGE evaluation metric. In terms of summary generation quality, the proposed method improves the accuracy and fluidity during the Chinese summarization process; in terms of model deployment requirements, the proposed method features a smaller parameter size, capable of meeting the needs for lightweight, low-overhead offline deployment.

Key words: text summarization, knowledge distillation, prompt engineering, large language model