LATE - GIL -nlp at S emeval-2025 Task 10: Exploring LLM s and transformers for Characterization and extraction of narratives from online news (2025)

Abstract: This paper tackles SemEval~2025 Task~10, “Multilingual Characterization and Extraction of Narratives from Online News,” focusing on the Ukraine-Russia War and Climate Change domains. Our approach covers three subtasks: (1) {textbf{Entity Framing}}, assigning protagonist-antagonist-innocent roles with a prompt-based Llama~3.1~(8B) method; (2) {textbf{Narrative Classification}}, a multi-label classification using XLM-RoBERTa-base; and (3) {textbf{Narrative Extraction}}, generating concise, text-grounded explanations via FLAN-T5. Results show a unified multilingual transformer pipeline, combined with targeted preprocessing and fine-tuning, achieves substantial gains over baselines while effectively capturing complex narrative structures despite data imbalance and varied label distributions.

## introduction this shared task "multilingual characterization and extraction of narratives from online news" @xcite tackles the analysis of news articles from the ukraine-russia war and climate change domains through three subtasks. subtask 1 -entity framing, assigning fine-grained roles (protagonists, antagonists, innocent) to named entities using a prompt-based llama 3.1 8b model (meta-ai, 2025); subtask 2 -narrative classification, labeling articles via a multi-label xlm-robertabase classifier (facebook-ai, 2019); and subtask 3 -narrative extraction, generating explanations for dominant narratives with google flan-t5 @xcite . these tasks are important for understanding how entities are portrayed and how broader narratives are constructed in multidomain, multilingual settings. our key findings indicate that, while pretrained language models help manage complex label sets in multiple languages, class imbalance and domain variability remain challenging. notable results include exact match ratios up to 0.33670 (portuguese) for subtask 1, f1 (samples) up to 0.16300 (english) for subtask 2, and macro-f1 scores up to 0.69558 (english) for subtask 3. the system generally excels at frequent labels yet struggles with rare ones, highlighting the need for more balanced data and refined approaches to boost performance across languages. ## background in the field of natural language processing (nlp), significant progress has been made in three key subtasks of semeval 2025 task 10: entity framing, narrative classification, and narrative extraction @xcite . these studies have used methodologies such as word embeddings analysis (e.g., word2vec and multilingual bert) to capture semantic similarities across languages, as well as clustering and topic modeling techniques (e.g., lda) to identify thematic patterns in large text corpora. for entity framing, studies such as @xcite developed wikipedia-based systems for entity extraction, classification, and tagging in social media, outperforming traditional approaches. in narrative classification, @xcite demonstrated the importance of narrative classification for effective key phrase extraction in web documents, using machine learning methods. in narrative extraction, keith norambuena et al. (2023) surveyed event-based techniques for extracting news narratives, highlighting their utility in analyzing evolving information landscapes. in the legal domain, samy (2021) created a linguistic resource for named entity recognition and classification (nerc) in spanish legal texts, combining regular expressions, external lists, and trained models. these studies employed tools such as machine learning models, transformers, and hybrid techniques, analyzing diverse texts, from news articles to social media posts and legal documents. ## conclusions our multi-stage architecture-binary filtering followed by multi-label narrative and subnarrative classification demonstrates strong performance on frequent categories and outperforms a baseline sys-tem. we evaluated three languages (english, bulgarian, and hindi) due to the availability and consistency of data, but the approach generalizes to other languages. effective capture of major narratives and high micro-f1 suggests robust coverage for well-represented classes. modular design allows the system to scale and adapt to multilingual contexts with minimal code changes. reliance on large amounts of representative data for adequate training, classes with fewer than 15 samples defaulted to a single subnarrative, limiting granularity. finally, we established a pipeline that constructs a model that can both classify and generate narrative explanations. we then fine-tuned a t5-based model for sequence-to-sequence tasks for narrative classification and explanation generation.