long context benchmark
SummScreenFD Leaderboard
SummScreenFD is the ForeverDreaming subset of the SummScreen dataset for abstractive screenplay summarization, comprising pairs of TV series transcripts and human-written recaps from 88 different shows. The dataset provides a challenging testbed for abstractive summarization where plot details are often expressed indirectly in character dialogues and scattered across the entirety of the transcript, requiring models to find and integrate these details to form succinct plot descriptions.
Updated Aug 17, 2026
Models2
Model coverage2
MetricScore
EvidenceB
SummScreenFD Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMI | Score16.9% | Percentile100.0% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelMI | Score16.0% | Percentile0.0% | Participants2 | EvidenceC | Evaluated |
SummScreenFD Highlights
The leading models and scores on this benchmark.
SummScreenFD Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for SummScreenFD
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis summscreenfd 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.
- 01MIPhi-3.5-MoE-instructMicrosoft
- Score
- 16.9%
Strengths
- Ranks #1 of 2 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures SummScreenFD, not total model capability
- 02MIPhi-3.5-mini-instructMicrosoft
- Score
- 16.0%
- Speed
- Up to 23 tok/s via Azure
Strengths
- Ranks #2 of 2 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures SummScreenFD, not total model capability
Selection summary
Best AI Models for SummScreenFD
Phi-3.5-MoE-instruct currently leads SummScreenFD with 16.9%. 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 SummScreenFD?
What SummScreenFD measures and how its scores work.
SummScreenFD is the ForeverDreaming subset of the SummScreen dataset for abstractive screenplay summarization, comprising pairs of TV series transcripts and human-written recaps from 88 different shows. The dataset provides a challenging testbed for abstractive summarization where plot details are often expressed indirectly in character dialogues and scattered across the entirety of the transcript, requiring models to find and integrate these details to form succinct plot descriptions.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- SummScreenFD
- Modality
- text
- Primary category
- long context
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- summscreenfd|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about SummScreenFD.
Which model scores highest on SummScreenFD?
Phi-3.5-MoE-instruct is currently ranked first with 16.9%.
What does SummScreenFD measure?
SummScreenFD is the ForeverDreaming subset of the SummScreen dataset for abstractive screenplay summarization, comprising pairs of TV series transcripts and human-written recaps from 88 different shows. The dataset provides a challenging testbed for abstractive summarization where plot details are often expressed indirectly in character dialogues and scattered across the entirety of the transcript, requiring models to find and integrate these details to form succinct plot descriptions.
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.