The advent of large language models (LLMs) has significantly advanced natural
language processing tasks like text summarization. However, their large size
and computational demands, coupled with privacy concerns in data transmission,
limit their use in resource-constrained and privacy-centric settings. To
overcome this, we introduce TriSum, a framework for distilling LLMs' text
summarization abilities into a compact, local model. Initially, LLMs extract a
set of aspect-triple rationales and summaries, which are refined using a
dual-scoring method for quality. Next, a smaller local model is trained with
these tasks, employing a curriculum learning strategy that evolves from simple
to complex tasks. Our method enhances local model performance on various
benchmarks (CNN/DailyMail, XSum, and ClinicalTrial), outperforming baselines by
4.5%, 8.5%, and 7.4%, respectively. It also improves interpretability by
providing insights into the summarization rationale.

通过 TriSum 框架，我们将大型语言模型的文本摘要能力提炼为一个紧凑且本地化的模型，以应对资源有限和注重隐私的环境。通过对各项任务进行演化式学习，我们的方法在各个评估基准上提升了本地化模型的性能，并提供了摘要的合理解释。