ReSUM - QMSum - Mover: leaderboard

ReSUM v1 Table5: query-focused long-meeting summarization on QMSum and SQuALITY; source-native zero-shot, CoT, RAG, fine-tuned and multi-agent methods remain separate run identities. Short source model labels (including LLaMA3-3B and GPT-4-mini) are not bound to a guessed checkpoint. R1/R2/RL, BERTScore, Mover and COMET are native 0–1 values; chrF is native percent. No transformation, model alias, cross-method pooling or resource metric.

Metric: Native score (0–1). Source: arxiv.org. Saturation forecast: Rough model projection: around 2026. 16 models tracked.

Top models

#ModelScoreOverall rank
1Our Method (ReSUM Run: method=ReSUM Multi-Agent;checkpoint=source-native;effort=unreported)0.63
2FT Qwen (ReSUM Run: method=Fine-tuned (FT);checkpoint=source-native;effort=unreported)0.6
3FT+Res Qwen (ReSUM Run: method=End-to-End Fine-tuned with Reasoning;checkpoint=source-native;effort=unreported)0.6
4FT LLaMA (ReSUM Run: method=Fine-tuned (FT);checkpoint=source-native;effort=unreported)0.56
5GPT-4-mini (ReSUM Run: method=Zero-shot;checkpoint=source-native;effort=unreported)0.55
6Qwen3-4B (ReSUM Run: method=Zero-shot;checkpoint=source-native;effort=unreported)0.54
7COT Qwen (ReSUM Run: method=Chain-of-Thought (CoT);checkpoint=source-native;effort=unreported)0.54
8RAG-FAISS (ReSUM Run: method=Retrieval-Augmented Generation (RAG);checkpoint=source-native;effort=unreported)0.54
9RAG-BM25 (ReSUM Run: method=Retrieval-Augmented Generation (RAG);checkpoint=source-native;effort=unreported)0.54
10RAG-HYBRID (ReSUM Run: method=Retrieval-Augmented Generation (RAG);checkpoint=source-native;effort=unreported)0.54

Interactive version: theaggregate.ai/benchmark?slug=resum-qmsum-mover · How It Works · Data refreshed daily, snapshot 2026-11-12.