Recently, a large number of tuning strategies have been proposed to adapt
pre-trained language models to downstream tasks. In this paper, we perform an
extensive empirical evaluation of various tuning strategies for multilingual
learning, particularly in the context of text summarization. Specifically, we
explore the relative advantages of three families of multilingual tuning
strategies (a total of five models) and empirically evaluate them for
summarization over 45 languages. Experimentally, we not only established a new
state-of-the-art on the XL-Sum dataset but also derive a series of observations
that hopefully can provide hints for future research on the design of
multilingual tuning strategies.

本文针对文本摘要领域的多语言学习，对三种多语言调优策略（共五个模型）进行广泛的实证评估，实验覆盖 45 种语言，旨在为多语言调优策略设计提供参考。实验结果在 XL-Sum 数据集上取得了新的最佳表现。

寻找有效的多语言微调方法：摘要中的案例研究

Searching for Effective Multilingual Fine-Tuning Methods: A Case Study in Summarization

Contemporary works on abstractive text summarization have focused primarily
on high-resource languages like English, mostly due to the limited availability
of datasets for low/mid-resource ones. In this work, we present XL-Sum, a
comprehensive and diverse dataset comprising 1 million professionally annotated
article-summary pairs from BBC, extracted using a set of carefully designed
heuristics. The dataset covers 44 languages ranging from low to high-resource,
for many of which no public dataset is currently available. XL-Sum is highly
abstractive, concise, and of high quality, as indicated by human and intrinsic
evaluation. We fine-tune mT5, a state-of-the-art pretrained multilingual model,
with XL-Sum and experiment on multilingual and low-resource summarization
tasks. XL-Sum induces competitive results compared to the ones obtained using
similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10
languages we benchmark on, with some of them exceeding 15, as obtained by
multilingual training. Additionally, training on low-resource languages
individually also provides competitive performance. To the best of our
knowledge, XL-Sum is the largest abstractive summarization dataset in terms of
the number of samples collected from a single source and the number of
languages covered. We are releasing our dataset and models to encourage future
research on multilingual abstractive summarization. The resources can be found
at https://github.com/csebuetnlp/xl-sum.

该研究介绍了一份名为 “XL-Sum” 的全面而多样化的数据集，其中包含来自 44 种语言的 100 万篇新闻文章及其摘要，旨在支持跨语言抽象文本摘要的未来研究工作，且在许多语言上可实现尤其高的 ROUGE-2 分数。