long context benchmark
QMSum
QMSum is a benchmark for query-based multi-domain meeting summarization consisting of 1,808 query-summary pairs over 232 meetings across academic, product, and committee domains. The dataset enables models to select and summarize relevant spans of meetings in response to specific queries. Published at NAACL 2021, QMSum presents significant challenges in long meeting summarization where models must identify and summarize relevant content based on user queries.
Updated Aug 11, 2026
Models2
Model coverage2
MetricScore
EvidenceB
QMSum Ranking
Higher score ranks better on this benchmark.
| 1 | MI | 21.3% | 100.0% | 2 | C | |
| 2 | MI | 19.9% | 0.0% | 2 | C |
QMSum Score Distribution
A closer view of the leading scores on this benchmark.
QMSum Highlights
The leading models and scores on this benchmark.
What is QMSum?
What QMSum measures and how its scores work.
QMSum is a benchmark for query-based multi-domain meeting summarization consisting of 1,808 query-summary pairs over 232 meetings across academic, product, and committee domains. The dataset enables models to select and summarize relevant spans of meetings in response to specific queries. Published at NAACL 2021, QMSum presents significant challenges in long meeting summarization where models must identify and summarize relevant content based on user queries.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- QMSum
- Modality
- text
- Primary category
- long context
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- qmsum|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about QMSum.
Which model scores highest on QMSum?
Phi-3.5-mini-instruct is currently ranked first with 21.3%.
What does QMSum measure?
QMSum is a benchmark for query-based multi-domain meeting summarization consisting of 1,808 query-summary pairs over 232 meetings across academic, product, and committee domains. The dataset enables models to select and summarize relevant spans of meetings in response to specific queries. Published at NAACL 2021, QMSum presents significant challenges in long meeting summarization where models must identify and summarize relevant content based on user queries.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 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.