@inproceedings{vijayachandran-etal-2026-iiith,
title = "{IIITH} Boys at {S}em{E}val-2026 Task 4: {S}tory{N}et - Understanding Narrative Story Similarity through Symbolic Representations",
author = "Vijayachandran, Amol and
Rajesh, Ananth and
Mago, Siddharth and
Chitale, Maitreya and
Allamraju, Aparajitha",
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-1.420/",
pages = "3382--3393",
ISBN = "979-8-89176-414-9",
abstract = "Narrative similarity extends beyond standard semantic tasks, requiring alignment of temporal, causal, and emotional structures. We present StoryNet, a framework that represents stories as heterogeneous graphs with character, event, and theme nodes. Stories are decomposed into structured narrative facets using large language models, and similarity is evaluated through both weighted semantic facet comparison and a graph neural network trained with contrastive learning. We analyze how integrating symbolic structure with learned graph representations compares to purely embedding-based baselines."
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<abstract>Narrative similarity extends beyond standard semantic tasks, requiring alignment of temporal, causal, and emotional structures. We present StoryNet, a framework that represents stories as heterogeneous graphs with character, event, and theme nodes. Stories are decomposed into structured narrative facets using large language models, and similarity is evaluated through both weighted semantic facet comparison and a graph neural network trained with contrastive learning. We analyze how integrating symbolic structure with learned graph representations compares to purely embedding-based baselines.</abstract>
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%0 Conference Proceedings
%T IIITH Boys at SemEval-2026 Task 4: StoryNet - Understanding Narrative Story Similarity through Symbolic Representations
%A Vijayachandran, Amol
%A Rajesh, Ananth
%A Mago, Siddharth
%A Chitale, Maitreya
%A Allamraju, Aparajitha
%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 vijayachandran-etal-2026-iiith
%X Narrative similarity extends beyond standard semantic tasks, requiring alignment of temporal, causal, and emotional structures. We present StoryNet, a framework that represents stories as heterogeneous graphs with character, event, and theme nodes. Stories are decomposed into structured narrative facets using large language models, and similarity is evaluated through both weighted semantic facet comparison and a graph neural network trained with contrastive learning. We analyze how integrating symbolic structure with learned graph representations compares to purely embedding-based baselines.
%U https://aclanthology.org/2026.semeval-1.420/
%P 3382-3393
Markdown (Informal)
[IIITH Boys at SemEval-2026 Task 4: StoryNet - Understanding Narrative Story Similarity through Symbolic Representations](https://aclanthology.org/2026.semeval-1.420/) (Vijayachandran et al., SemEval 2026)
ACL