FaNS: a Facet-based Narrative Similarity Metric
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910349936033792 |
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| author | Akter, Mousumi Santu, Shubhra Kanti Karmaker |
| author_facet | Akter, Mousumi Santu, Shubhra Kanti Karmaker |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_04823 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | FaNS: a Facet-based Narrative Similarity Metric Akter, Mousumi Santu, Shubhra Kanti Karmaker Computation and Language 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. |
| title | FaNS: a Facet-based Narrative Similarity Metric |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2309.04823 |