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    <abstract>This paper presents our approaches for three subtasks in SemEval-2025 Task 10, which focus on entity framing, narrative classification, and narrative extraction in new analysis respectively. We propose a two-stage news analytical framework for both Subtask A and B. In Subtask A (Entity Framing), we design an entity-oriented data processing pipeline to address the issue of redundant information in a news article, and explore effective use of multilingual datasets through sufficient experiments. The system achieves the first place in Bulgarian and the second place in English and Portuguese. In Subtask B (Narrative Classification), a similar narrative-oriented data processing pipeline is adopted to obtain condensed news chunks for each narrative. We conduct in-depth discussion regarding approaches to enhancing both data quality and volume, and explore one-vs-rest classification models and sequence prediction models for multi-label classification tasks. The system ranks first in Bulgarian and second in Russian and Portuguese. In Subtask 3 (Narrative Extraction), we build our system with data augmentation, supervised fine-tuning, and preference-based reinforcement learning. This system achieves the first place in Bulgarian, Russian and Hindi and the second place in Portuguese.</abstract>
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%0 Conference Proceedings
%T PATeam at SemEval-2025 Task 10: Two-stage News Analytical Framework: Target-oriented Semantic Segmentation and Sequence Generation LLMs for Cross-Lingual Entity and Narrative Analysis
%A Sun, Ling
%A Wan, Xue
%A Lin, Yuyang
%A Su, Fengping
%A Chen, Pengfei
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F sun-etal-2025-pateam
%X This paper presents our approaches for three subtasks in SemEval-2025 Task 10, which focus on entity framing, narrative classification, and narrative extraction in new analysis respectively. We propose a two-stage news analytical framework for both Subtask A and B. In Subtask A (Entity Framing), we design an entity-oriented data processing pipeline to address the issue of redundant information in a news article, and explore effective use of multilingual datasets through sufficient experiments. The system achieves the first place in Bulgarian and the second place in English and Portuguese. In Subtask B (Narrative Classification), a similar narrative-oriented data processing pipeline is adopted to obtain condensed news chunks for each narrative. We conduct in-depth discussion regarding approaches to enhancing both data quality and volume, and explore one-vs-rest classification models and sequence prediction models for multi-label classification tasks. The system ranks first in Bulgarian and second in Russian and Portuguese. In Subtask 3 (Narrative Extraction), we build our system with data augmentation, supervised fine-tuning, and preference-based reinforcement learning. This system achieves the first place in Bulgarian, Russian and Hindi and the second place in Portuguese.
%U https://aclanthology.org/2025.semeval-1.276/
%P 2121-2132

Markdown (Informal)

[PATeam at SemEval-2025 Task 10: Two-stage News Analytical Framework: Target-oriented Semantic Segmentation and Sequence Generation LLMs for Cross-Lingual Entity and Narrative Analysis](https://aclanthology.org/2025.semeval-1.276/) (Sun et al., SemEval 2025)

• PATeam at SemEval-2025 Task 10: Two-stage News Analytical Framework: Target-oriented Semantic Segmentation and Sequence Generation LLMs for Cross-Lingual Entity and Narrative Analysis (Sun et al., SemEval 2025)

ACL

• Ling Sun, Xue Wan, Yuyang Lin, Fengping Su, and Pengfei Chen. 2025. PATeam at SemEval-2025 Task 10: Two-stage News Analytical Framework: Target-oriented Semantic Segmentation and Sequence Generation LLMs for Cross-Lingual Entity and Narrative Analysis. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2121–2132, Vienna, Austria. Association for Computational Linguistics.