# MLSUM: The Multilingual Summarization Corpus

arXiv: https://arxiv.org/abs/2004.14900 · HTML: https://mlsanity.com/paper/2004.14900/

## Key facts

- Cited by 121 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 30 of 121)

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- [EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://mlsanity.com/paper/2210.13448/) (2022)
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- [Language and Task Arithmetic with Parameter-Efficient Layers for Zero-Shot Summarization](https://mlsanity.com/paper/2311.09344/) (2023)
- [Revisiting non-English Text Simplification: A Unified Multilingual Benchmark](https://mlsanity.com/paper/2305.15678/) (2023)
- [Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignment](https://mlsanity.com/paper/2404.12318/) (2024)
- [On the Multilingual Capabilities of Very Large-Scale English Language Models](https://mlsanity.com/paper/2108.13349/) (2021)
- [How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?](https://mlsanity.com/paper/2406.11477/) (2024)
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- [An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference](https://mlsanity.com/paper/2402.10712/) (2024)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)

## Abstract (arXiv)

We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.