Similar Narrative Retrieval is a crucial task since narratives are essential for explaining and understanding events, and multiple related narratives often help to create a holistic view of the event of interest. To accurately identify semantically similar narratives, this paper proposes a novel narrative similarity metric called Facet-based Narrative Similarity (FaNS), based on the classic 5W1H facets (Who, What, When, Where, Why, and How), which are extracted by leveraging the state-of-the-art Large Language Models (LLMs). Unlike existing similarity metrics that only focus on overall lexical/semantic match, FaNS provides a more granular matching along six different facets independently and then combines them. To evaluate FaNS, we created a comprehensive dataset by collecting narratives from AllSides, a third-party news portal. Experimental results demonstrate that the FaNS metric exhibits a higher correlation (37\% higher) than traditional text similarity metrics that directly measure the lexical/semantic match between narratives, demonstrating its effectiveness in comparing the finer details between a pair of narratives.

翻译：摘要：相似叙事检索是一项关键任务，因为叙事对于解释和理解事件至关重要，而多个相互关联的叙事通常有助于形成对所关注事件的全面认识。为了准确识别语义相似的叙事，本文提出了一种新颖的叙事相似度度量方法——基于要素的叙事相似度（Facet-based Narrative Similarity, FaNS），该方法基于经典的5W1H要素（何人、何事、何时、何地、为何及如何），通过利用最先进的大型语言模型（LLMs）进行提取。与仅关注整体词汇/语义匹配的现有相似度度量不同，FaNS分别从六个不同要素维度进行更细粒度的匹配，随后将其组合。为评估FaNS，我们通过收集第三方新闻门户网站AllSides的叙事构建了一个综合性数据集。实验结果表明，与传统直接衡量叙事间词汇/语义匹配的文本相似度度量相比，FaNS度量呈现出更高的相关性（高出37%），验证了其在比较一对叙事间更细微细节方面的有效性。