Year: 2026
Type: conference-paper
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.
Authors: Janis Pagel, Nils Reiter
Institution: University of Cologne
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166815064