  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.
