RESEARCHMonitorWATCHLIST
Preserving What Matters: Semantic Scaffolds Beyond Saturation in Summarization Evaluation
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Research finds standard summarization metrics like ROUGE and LLM-as-judge suffer from score saturation, failing to rank models effectively.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 22 September 2026
- Collected by OneBench
- 23 Sept 2026, 03:01 UK
Stored source excerpt
arXiv:2609.22603v1 Announce Type: new Abstract: Summarization ships in countless production systems, making model selection a routine decision that depends on measuring summary quality. Existing metrics…
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