识别新闻故事中的核心组织，让机器理解谁在推动情节发展。
Who Is the Story About? Protagonist Entity Recognition in News
- 提出新任务PER，识别新闻中真正主导叙事的组织实体。
- 专家与大模型标注一致率高，验证了判断标准可靠性。
- 用大模型自动生成大规模高质量标注数据，适合做叙事分析。
新闻文章常提及众多组织，但传统命名实体识别（NER）对所有提及同等对待，模糊了真正驱动叙事的核心主体，限制了事件重要性、影响力或叙事焦点等下游任务的理解。本文提出主角实体识别（Protagonist Entity Recognition, PER），旨在识别锚定新闻故事、塑造其主要发展的组织实体。为验证PER，我们对比四位专家标注与大语言模型（LLM）预测在黄金语料上的结果，建立了标注者间一致性及人机一致性。基于此，我们利用先进大模型通过基于NER的提示策略，自动标注大规模新闻集合，生成可扩展的高质量监督信号。随后评估其他大模型在有限上下文、无显式候选指导的情况下，是否仍能推断出正确主角。结果表明，PER是可行且有意义的叙事中心信息抽取延伸，且经过引导的大模型可在大规模上近似人类对叙事重要性的判断。
原文摘要 · Abstract (English)
News articles often reference numerous organizations, but traditional Named Entity Recognition (NER) treats all mentions equally, obscuring which entities genuinely drive the narrative. This limits downstream tasks that rely on understanding event salience, influence, or narrative focus. We introduce Protagonist Entity Recognition (PER), a task that identifies the organizations that anchor a news story and shape its main developments. To validate PER, we compare he predictions of Large Language Models (LLMs) against annotations from four expert annotators over a gold corpus, establishing both inter-annotator consistency and human-LLM agreement. Leveraging these findings, we use state-of-the-art LLMs to automatically label large-scale news collections through NER-guided prompting, generating scalable, high-quality supervision. We then evaluate whether other LLMs, given reduced context and without explicit candidate guidance, can still infer the correct protagonists. Our results demonstrate that PER is a feasible and meaningful extension to narrative-centered information extraction, and that guided LLMs can approximate human judgments of narrative importance at scale.
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