  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 \url{https://github.com/csebuetnlp/xl-sum}.
