@inproceedings{rosenfeld-etal-2026-jct,
title = "{JCT} at {S}em{E}val-2026 Task 4: A Multi-Method Approach to Narrative Story Similarity",
author = "Rosenfeld, Dvori and
Walles, Rinat and
Liebeskind, Chaya",
editor = "Kochmar, Ekaterina and
Ghosh, Debanjan and
North, Kai and
Komachi, Mamoru",
booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.semeval-1.387/",
pages = "3089--3094",
ISBN = "979-8-89176-414-9",
abstract = "Narrative similarity detection involves under-standing the underlying structure of a storyrather than just matching similar words orphrases. This paper details our multi-strategyapproach to the SemEval-2026 Task on Nar-rative Similarity, which requires identifyingwhich of two candidate stories most closelyresembles an anchor story based on three di-mensions: abstract themes, the sequence ofevents, and the final outcomes.We developed three distinct but complemen-tary methods to address this challenge. First,we fine-tuned a specialized story-embeddingmodel using parameter-efficient techniques onsynthetic data. Second, we utilized a ``Distill-then-Embed'' workflow, where a large languagemodel extracts the essential narrative core ofeach story before computing similarity. Third,we employed direct zero-shot prompting to al-low a high-reasoning model to compare thestories organically.Our analysis reveals that each approach excelsat different types of narrative comparisons, andtheir combination leads to robust performance.We demonstrate the importance of narrative dis-tillation in removing surface-level distractorsand the effectiveness of carefully engineeredprompts in guiding language models to focuson narrative structure"
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<abstract>Narrative similarity detection involves under-standing the underlying structure of a storyrather than just matching similar words orphrases. This paper details our multi-strategyapproach to the SemEval-2026 Task on Nar-rative Similarity, which requires identifyingwhich of two candidate stories most closelyresembles an anchor story based on three di-mensions: abstract themes, the sequence ofevents, and the final outcomes.We developed three distinct but complemen-tary methods to address this challenge. First,we fine-tuned a specialized story-embeddingmodel using parameter-efficient techniques onsynthetic data. Second, we utilized a “Distill-then-Embed” workflow, where a large languagemodel extracts the essential narrative core ofeach story before computing similarity. Third,we employed direct zero-shot prompting to al-low a high-reasoning model to compare thestories organically.Our analysis reveals that each approach excelsat different types of narrative comparisons, andtheir combination leads to robust performance.We demonstrate the importance of narrative dis-tillation in removing surface-level distractorsand the effectiveness of carefully engineeredprompts in guiding language models to focuson narrative structure</abstract>
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%0 Conference Proceedings
%T JCT at SemEval-2026 Task 4: A Multi-Method Approach to Narrative Story Similarity
%A Rosenfeld, Dvori
%A Walles, Rinat
%A Liebeskind, Chaya
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F rosenfeld-etal-2026-jct
%X Narrative similarity detection involves under-standing the underlying structure of a storyrather than just matching similar words orphrases. This paper details our multi-strategyapproach to the SemEval-2026 Task on Nar-rative Similarity, which requires identifyingwhich of two candidate stories most closelyresembles an anchor story based on three di-mensions: abstract themes, the sequence ofevents, and the final outcomes.We developed three distinct but complemen-tary methods to address this challenge. First,we fine-tuned a specialized story-embeddingmodel using parameter-efficient techniques onsynthetic data. Second, we utilized a “Distill-then-Embed” workflow, where a large languagemodel extracts the essential narrative core ofeach story before computing similarity. Third,we employed direct zero-shot prompting to al-low a high-reasoning model to compare thestories organically.Our analysis reveals that each approach excelsat different types of narrative comparisons, andtheir combination leads to robust performance.We demonstrate the importance of narrative dis-tillation in removing surface-level distractorsand the effectiveness of carefully engineeredprompts in guiding language models to focuson narrative structure
%U https://aclanthology.org/2026.semeval-1.387/
%P 3089-3094
Markdown (Informal)
[JCT at SemEval-2026 Task 4: A Multi-Method Approach to Narrative Story Similarity](https://aclanthology.org/2026.semeval-1.387/) (Rosenfeld et al., SemEval 2026)
ACL