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