Abstract: In the field of natural language processing, large language models are currently witnessing vigorous development. However, in the process of application in educational digitization, a series of important challenges still exist. Aiming at addressing the problem posed by the scarcity of domain-specific data, unstable summarization leading to information loss or redundancy, a lightweight idempotent model framework, IGLM, is introduced for educational text summarization. The model first employs multi-source training for adaptive augmentation to enhance data diversity. Subsequently, various fine-tuning procedures are applied to the downstream text summarization task. Concurrently, an idempotent summarization generation strategy is designed to mitigate the impact of text length, the initial summaries are brought closer to idempotent summaries to constrain the model and mitigate biases resulting from uneven language corpora and combining quantization techniques to generate more precise and fluent summary texts under low-resource conditions. The experiments use ROUGE F1 scores as the evaluation metric and validate on the publicly available Chinese text summarization datasets, LCSTS, EDUCATION and NLPCC. The results of experiments reveal significant enhancements in precision and coherence within this framework. Specifically, in comparison to the baseline model, the ROUGE-1/2/L scores experienced respective increases of 7.9, 7.4, and 8.7 on the LCSTS dataset. Moreover, on the EDUCATION dataset, the scores exhibited enhancements of 12.9, 15.4, and 15.7 for ROUGE-1/2/L, respectively. Similarly, on the NLPCC dataset, there were improvements of 12.2, 11.7, and 12.7 for ROUGE-1/2/L, respectively. This validation confirms the model's efficacy, offering a robust solution for educational digitalization tasks.

摘要： 大型语言模型在自然语言处理领域蓬勃发展，但在教育数字化领域应用过程中仍面临一系列重要挑战。针对教育数字化领域垂域数据稀缺、摘要长度不稳定导致信息缺失或冗余的问题，提出了一种用于教育领域摘要的轻量化幂等模型框架IGLM。该模型首先采用多源训练进行自适应扩增以增加数据多样性，然后对下游的文本摘要任务进行多种微调。同时，为降低文本长度的影响设计幂等摘要生成策略拉近初次摘要与幂等摘要来约束模型，减少语料分布不均导致的偏见，结合量化技术在低资源条件下生成更为精确和流畅的摘要文本。实验以ROUGE F1分数为评估指标，在公开中文文本摘要数据集LCSTS、EDUCATION、NLPCC上进行验证。实验结果表明，该框架在生成摘要的准确率和流畅性上有明显提升，其中ROUGE-1/2/L相较基线模型在LCSTS数据集上分别提高了7.9、7.4和8.7，在EDUCATION数据集上分别有了12.9、15.4、15.7的提升，在NLPCC数据集上分别提高了12.2、11.7、12.7，从而验证了模型有效性，为教育数字化工作提供了有效的解决方案。