N arrative N exus at S em E val-2025 Task 10: Entity Framing and Narrative Extraction using BART (2025)

Abstract: This paper presents NarrativeNexus’ participation in SemEval-2025 Task 10 on fine-grained entity framing and narrative extraction. Our approach utilizes BART, a transformer-based encoder-decoder model, fine-tuned for sequence classification and text generation.For Subtask 1, we employed a BART-based sequence classifier to identify and categorize named entities within news articles, mapping them to predefined roles such as protagonists, antagonists, and innocents. In Subtask 3, we leveraged a text-to-text generative approach to generate justifications for dominant narratives.Our methodology included hyperparameter tuning, data augmentation, and ablation studies to assess model components. NarrativeNexus achieved 18th place in Subtask 1 and 10th in Subtask 3 on the English dataset. Our findings highlight the strengths of pre-trained transformers in structured content analysis while identifying areas for future improvements in nuanced entity framing.

## introduction entity framing and narrative extraction are essential tasks in natural language processing (nlp), enabling applications in media bias detection, sentiment tracking, and sociopolitical discourse analysis. semeval-2025 task 10 comprises three subtasks, of which we addressed two: (1) entity framing and (3) narrative extraction. subtask 1 required classification of named entities into predefined roles, while subtask 3 focused on generating textual justifications for dominant narratives in news articles. we used bart, a pre-trained transformer model, fine-tuned separately for sequence classification and text generation. despite bart's strong baseline capabilities, additional techniques such as data augmentation, hallucination filtering, and post-processing were necessary to address domainspecific challenges. ## related work we reviewed and analyzed previous research on entity framing and narrative extraction, categorizing studies based on their methodologies and objectives. sinelnik and hovy (sinelnik and hovy, 2024) explored multilingual disinformation framing using xlm-roberta, effectively detecting thematic frames across multiple languages. their work highlights the challenges of multilingual preprocessing and the importance of aligning embeddings with linguistic differences. xu et al. @xcite introduced narco, a graph-based transformer-xl model designed for narrative coherence. their approach improved causal and temporal dependencies in long-form texts, significantly enhancing narrative consistency in text generation tasks. papalampidi et al. @xcite proposed a dynamic entity memory mechanism within a transformer-xl framework. by tracking entity attributes throughout a narrative, their model ensured coherence and consistency in generated texts, making it valuable for long-form content generation. schäfer et al. @xcite applied bertopic for fake news analysis, demonstrating its superiority over traditional topic modeling methods like lda and nmf. their study uncovered nuanced themes within misinformation campaigns, improving content classification and thematic anal-ysis. sinelnik and hovy (sinelnik and hovy, 2024) also examined lexicon-based approaches for frame detection. while these methods offer interpretability and lightweight computation, they struggle to capture rare or evolving thematic frames, limiting their effectiveness for complex analyses. these studies collectively contribute to advancing nlp methodologies for entity framing, misinformation analysis, and narrative generation. our work builds upon these insights, leveraging transformer-based models for structured content analysis in semeval-2025 task 10. ## background semeval-2025 task 10 consists of three subtasks. we participated in: the english dataset was provided by the task organizers, along with gold-standard annotations. detailed task descriptions are available in the official task paper @xcite . ## dataset and preprocessing the english dataset included approximately: for subtask 1, we used pre-extracted context passages accompanying each entity in the dataset. these were paired with the corresponding entity and formatted as 'entity: entity. context: context' for bart input. for subtask 3, documents were truncated to 1,024 tokens using the hugging face tokenizer's built-in truncation mechanism, with narrative and subnarrative prepended to the article. ## results the performance of our system on the official test sets is presented in for subtask 3, our system ranked 10th, achieving a precision of 0.71991, recall of 0.74267, and an f1 macro score of 0.73085. the model demonstrated strong coherence in narrative justification generation, though further refinements in dataset curation and text conditioning techniques could boost performance further. these results emphasize the effectiveness of transformer-based architectures in entity framing and narrative generation while highlighting areas where improvements in feature extraction and model fine-tuning could lead to higher accuracy. ## conclusion we presented narrativenexus' approach to semeval-2025 task 10, focusing on entity framing and narrative extraction using bart. our findings highlight the potential of pre-trained transformer models for structured content analysis, particularly in maintaining narrative coherence. however, challenges such as fine-grained role differentiation and ensuring factual consistency in justification generation remain areas for future improvement. we aim to explore alternative model architectures, data augmentation strategies, and more refined evaluation techniques in future work to enhance entity framing and narrative extraction capabilities. the insights gained from this work contribute to advancing au-tomated media analysis and nlp applications in discourse understanding.