TriSum: Learning Summarization Ability from Large Language Models with
  Structured Rationale
Pengcheng Jiang Cao Xiao Zifeng Wang Parminder Bhatia Jimeng Sun Jiawei Han
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.