Summary sentences produced by abstractive summarization models may be
coherent and comprehensive, but they lack control and rely heavily on reference
summaries. The BRIO training paradigm assumes a non-deterministic distribution
to reduce the model's dependence on reference summaries, and improve model
performance during inference. This paper presents a straightforward but
effective technique to improve abstractive summaries by fine-tuning pre-trained
language models, and training them with the BRIO paradigm. We build a text
summarization dataset for Vietnamese, called VieSum. We perform experiments
with abstractive summarization models trained with the BRIO paradigm on the
CNNDM and the VieSum datasets. The results show that the models, trained on
basic hardware, outperform all existing abstractive summarization models,
especially for Vietnamese.

本文提出了一种简单但有效的技术，通过微调预先训练的语言模型并使用 BRIO 范例进行训练，以改进抽象摘要。我们在越南文上构建了一个文本摘要数据集，称为 VieSum，并使用 BRIO 范例在 CNNDM 和 VieSum 数据集上进行了实验。结果显示，这些模型在基本硬件上训练得到了提高，比所有现有的抽象摘要模型表现更好，尤其是对于越南文。