@inproceedings{pagel-reiter-2026-spinfo,
title = "Spinfo Cologne at {S}em{E}val-2026 Task 4: Explainable Creation of Narrativity Embeddings",
author = "Pagel, Janis and
Reiter, Nils",
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-1.299/",
pages = "2376--2383",
ISBN = "979-8-89176-414-9",
abstract = "We describe our submission to SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning.The task requires (i) selecting, for a given anchor story summary, which of two candidate summaries is narratively closer (Track A) and (ii) producing a narrative representation of a story as a vector embedding (Track B).Our approach emphasizes interpretability by explicitly eliciting three narrativity aspects with a prompted large language model.We then construct a fixed-size narrative embedding by concatenating aspect-wise representations, comparing a static-embedding baseline (GloVe) to contextualized sentence-transformer embeddings (all-MiniLM-L6-v2).On the development set, the sentence-transformer variant outperforms the static baseline and achieves 61.5{\%} accuracy on Track A, while the GloVe variant performs near chance.Our official submission reaches 60.25{\%} accuracy on the Track A test set and 57.75{\%} accuracy on Track B.Additional ablations show that the aspect pipeline slightly outperforms raw-text embeddings, but that aspect contributions are uneven.Qualitative analysis suggests that failures often stem from inconsistent aspect generation and from overemphasizing theme overlap over event-level similarity."
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<abstract>We describe our submission to SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning.The task requires (i) selecting, for a given anchor story summary, which of two candidate summaries is narratively closer (Track A) and (ii) producing a narrative representation of a story as a vector embedding (Track B).Our approach emphasizes interpretability by explicitly eliciting three narrativity aspects with a prompted large language model.We then construct a fixed-size narrative embedding by concatenating aspect-wise representations, comparing a static-embedding baseline (GloVe) to contextualized sentence-transformer embeddings (all-MiniLM-L6-v2).On the development set, the sentence-transformer variant outperforms the static baseline and achieves 61.5% accuracy on Track A, while the GloVe variant performs near chance.Our official submission reaches 60.25% accuracy on the Track A test set and 57.75% accuracy on Track B.Additional ablations show that the aspect pipeline slightly outperforms raw-text embeddings, but that aspect contributions are uneven.Qualitative analysis suggests that failures often stem from inconsistent aspect generation and from overemphasizing theme overlap over event-level similarity.</abstract>
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%0 Conference Proceedings
%T Spinfo Cologne at SemEval-2026 Task 4: Explainable Creation of Narrativity Embeddings
%A Pagel, Janis
%A Reiter, Nils
%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 pagel-reiter-2026-spinfo
%X We describe our submission to SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning.The task requires (i) selecting, for a given anchor story summary, which of two candidate summaries is narratively closer (Track A) and (ii) producing a narrative representation of a story as a vector embedding (Track B).Our approach emphasizes interpretability by explicitly eliciting three narrativity aspects with a prompted large language model.We then construct a fixed-size narrative embedding by concatenating aspect-wise representations, comparing a static-embedding baseline (GloVe) to contextualized sentence-transformer embeddings (all-MiniLM-L6-v2).On the development set, the sentence-transformer variant outperforms the static baseline and achieves 61.5% accuracy on Track A, while the GloVe variant performs near chance.Our official submission reaches 60.25% accuracy on the Track A test set and 57.75% accuracy on Track B.Additional ablations show that the aspect pipeline slightly outperforms raw-text embeddings, but that aspect contributions are uneven.Qualitative analysis suggests that failures often stem from inconsistent aspect generation and from overemphasizing theme overlap over event-level similarity.
%U https://aclanthology.org/2026.semeval-1.299/
%P 2376-2383
Markdown (Informal)
[Spinfo Cologne at SemEval-2026 Task 4: Explainable Creation of Narrativity Embeddings](https://aclanthology.org/2026.semeval-1.299/) (Pagel & Reiter, SemEval 2026)
ACL