To further improve the performance of Tibetan text automatic summarization, the paper proposes a Tibetan text summarization model TiDiffuSum, which integrates CINO-LoRA and Self-condition into the DiffuSum to address issues of insufficient sentence representation, large parameter scale limiting contextual modeling, and high training costs in the Tibetan task. TiDiffuSum model introduces CINO-LoRA mechanism into the sentence encoder to enhance Tibetan semantic representation and significantly reduce the number of training parameters. Additionally, it incorporates Self-condition strategy in the diffusion generation module to strengthen the comprehension and utilization of contextual semantics. Experimental results indicate that TiDiffuSum can effectively reduce the parameter count to 0.45% of the baseline model on the Tibetan summarization dataset (TSUM), and achieves improvements of 1.07, 0.78, and 1.08 in ROUGE-1, ROUGE-2, and ROUGE-L scores, significantly outperforming baseline models.

为进一步提升藏文文本自动摘要的性能, 针对DiffuSum模型在藏文摘要任务中因句子表征能力不足、参数规模过大导致的上下文建模受限以及训练成本高等问题, 提出一种融合CINO-LoRA与自调节(Self-condition)的藏文文本自动摘要模型TiDiffuSum。该模型在句子编码器中引入CINO-LoRA机制, 以增强藏文语义表征并显著减少训练参数量; 在扩散生成模块中集成Self-condition策略, 加强对上下文语义的理解与利用。实验结果表明, TiDiffuSum在藏文摘要数据集TSUM上能够将参数量有效压缩至基线模型的0.45%, 且ROUGE-1, ROUGE-2和ROUGE-L指标分别提升1.07, 0.78和1.08, 显著优于基线模型。