L3IRIT at SemEval-2026 Task 4: Learning Narrative Similarity from Aligned Film Plot Summaries

AbstractThis paper presents the participation of the L3IRIT team in SemEval Task 4.The team is a joint research group working on narrative extraction from historical text, led by the IRIT laboratory (University of Toulouse) and the L3i laboratory (University of La Rochelle). Our participation is grounded in the construction of a novel bilingual resource extracted from Wikipedia by automatically aligning film plots. Leveraging this dataset, we train embedding models using contrastive learning objectives to capture higher-level narrative structures more effectively. The resulting resource goes beyond surface-level lexical overlap, providing supervision for narrative similarity without manual annotation. In addition, we introduce a named-entity masking strategy designed to promote narrative abstraction and reduce superficial entity-based matching. Overall, our approach aims to support representation learning that captures structural and event-level similarities across stories in different languages more effectively.Our system ranked in 24 of the 44 scoreboards for Task A and 20 of the 27 scoreboards for Task B, achieving accuracies of 65.75 and 61.00, respectively.