Papers by Zhangyue Jiang

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Challenge:	Recent studies have explored diffusion models for extractive summarization task, showcasing their remarkable capabilities.
Approach:	They propose a term-guided diffusion model for extractive summarization of legal documents that incorporates legal terminology into the model via a well-designed multifactor fusion noise weighting schedule.
Outcome:	The proposed model outperforms existing models on a self-constructed legal summarization dataset and achieves improvements of 3.10, 2.84, and 2.89 on three public datasets.