CrossSum
a large-scale dataset comprising 1.65 million cross-lingual article-summary samples in 1500+ language-pairs
Multiple Institutions
CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs
summarization
preprint
Tahmid Hasan, Abhik Bhattacharjee, Wasi Uddin Ahmad, Yuan-Fang Li, Yong-Bin Kang, Rifat Shahriyar

We present CrossSum, a large-scale dataset
comprising 1.65 million cross-lingual articlesummary samples in 1500+ language-pairs
constituting 45 languages. We use the multilingual XL-Sum dataset and align identical articles written in different languages via crosslingual retrieval using a language-agnostic representation model. We propose a multi-stage
data sampling algorithm and fine-tune mT5,
a multilingual pretrained model, with explicit
cross-lingual supervision with CrossSum and
introduce a new metric for evaluating crosslingual summarization. Results on established
and our proposed metrics indicate that models
fine-tuned on CrossSum outperforms summarization+translation baselines, even when the
source and target language pairs are linguistically distant. To the best of our knowledge,
CrossSum is the largest cross-lingual summarization dataset and also the first-ever that does
not rely on English as the pivot language. We
are releasing the dataset, alignment and training scripts, and the models to spur future research on cross-lingual abstractive summarization. The resources can be found at https:
//github.com/csebuetnlp/CrossSum.
