摘要生成尚未消亡
Summarization is Not Dead Yet
AI总结：
通过多维度评估，发现人类参考摘要在信息量和忠实度上仍优于大语言模型，后者仅在表面连贯性和流畅性上占优，表明摘要生成研究仍有挑战。
摘要：
大型语言模型（LLMs）的进展引发了关于模型生成的摘要可与人类撰写的参考摘要相媲美甚至超越后者的说法，这引发了摘要生成是否仍是一个开放研究问题的疑问。我们通过多轨道评估重新审视这一说法，涵盖五个不同数据集和五个最先进的LLMs，结合受控人工评估、偏差缓解的LLM作为评判协议、基于外部知识的事实性验证以及语料库级别的语言分析。我们的发现揭示了一个更为细致的图景：人类参考摘要继续在信息量和忠实度方面展现出优势，而LLM输出主要在表面连贯性和流畅性上更受青睐。事实性验证表明，人类参考摘要仍然更可靠，尤其是对于涉及推理或综合的声明，而语言分析揭示了不同模型之间风格同质化的模式。这些观察表明，当前的LLMs提高了摘要生成的质量下限，但其性能上限仍低于人类能力。
英文摘要：
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 diverse datasets and 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 references 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
