融合CINO-LoRA和Self-condition的DiffuSum藏文文本自动摘要OA

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.