Name: GEM - XLSum
Description: Large-Scale Multilingual Abstractive Summarization for 44 Languages" 
Provider: Multiple institutions
Paper Title: XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44
  Languages
Tasks: summarization
Venue Name: Findings of the Association for Computational Linguistics
Authors: Tahmid Hasan,Abhik Bhattacharjee,Md. Saiful Islam,Kazi Samin,Yuan-Fang Li,Yong-Bin Kang,M. Rahman,Rifat Shahriyar
Abstract: Contemporary works on abstractive text summarization have focused primarily on highresource languages like English, mostly due to the limited availability of datasets for low/midresource ones. In this work, we present XLSum, 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-theart pretrained multilingual model, with XLSum and experiment on multilingual and lowresource 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.