Summarization is Not Dead Yet

• URL: http://arxiv.org/abs/2606.08000v1
• Date: Sat, 06 Jun 2026 06:38:35 GMT
• Title: Summarization is Not Dead Yet
• Abstract summary: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references.<n>We re-examine this narrative through a multi-track evaluation covering five diverse datasets and five state-of-the-art LLMs.<n>Our findings reveal a more nuanced landscape in which human reference summaries continue to demonstrate advantages in informativeness and faithfulness.
• Score: 28.302567995407532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The progress of large language models (LLMs) has fueled claims that model-generated summaries rival or even surpass human-written references, raising questions about whether summarization remains an open research problem. We re-examine this narrative through a multi-track evaluation covering five diverse datasets and five state-of-the-art LLMs, combining controlled human assessment, bias-mitigated LLM-as-Judge protocols, factuality verification against external knowledge, and corpus-level linguistic analysis. Our findings reveal a more nuanced landscape in which human reference summaries continue to demonstrate advantages in informativeness and faithfulness, whereas LLM outputs are preferred mainly for surface-level coherence and fluency. Factuality verification indicates that human references remain more reliable, particularly for claims involving reasoning or synthesis, and linguistic analysis uncovers a pattern of stylistic homogeneity across different models. These observations suggest that current LLMs have raised the floor of summarization quality, but the ceiling of their performance remains below human capabilities.

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