language benchmark
Large-scale multilingual abstractive summarization dataset comprising 1 million professionally annotated article-summary pairs from BBC, covering 44 languages. XL-Sum is highly abstractive, concise, and of high quality, designed to encourage research on multilingual abstractive summarization tasks.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | NV | 31.6% | 100.0% | 1 | C |
The leading models and scores on this benchmark.
What XLSum English measures and how its scores work.
Large-scale multilingual abstractive summarization dataset comprising 1 million professionally annotated article-summary pairs from BBC, covering 44 languages. XL-Sum is highly abstractive, concise, and of high quality, designed to encourage research on multilingual abstractive summarization tasks.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about XLSum English.
Llama 3.1 Nemotron 70B Instruct is currently ranked first with 31.6%.
Large-scale multilingual abstractive summarization dataset comprising 1 million professionally annotated article-summary pairs from BBC, covering 44 languages. XL-Sum is highly abstractive, concise, and of high quality, designed to encourage research on multilingual abstractive summarization tasks.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.