summarization benchmark
XLSum English Leaderboard
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 17, 2026
Models1
Model coverage1
MetricScore
EvidenceB
XLSum English Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelNV | Score31.6% | Percentile100.0% | Participants1 | EvidenceC | Evaluated |
XLSum English Highlights
The leading models and scores on this benchmark.
The Top AI Models for XLSum English
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis xlsum english AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01NV
- Score
- 31.6%
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures XLSum English, not total model capability
Selection summary
Best AI Models for XLSum English
Llama 3.1 Nemotron 70B Instruct currently leads XLSum English with 31.6%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is XLSum English?
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.
- Family
- XLSum English
- Modality
- text
- Primary category
- summarization
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- xlsum-english|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about XLSum English.
Which model scores highest on XLSum English?
Llama 3.1 Nemotron 70B Instruct is currently ranked first with 31.6%.
What does XLSum English measure?
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.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.