@inproceedings{siraj-etal-2025-narrativenexus,
title = "{N}arrative{N}exus at {S}em{E}val-2025 Task 10: Entity Framing and Narrative Extraction using {BART}",
author = "Siraj, Hareem and
Chandani, Kushal and
Sameen, Dua E and
Enayat, Ayesha",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.186/",
pages = "1411--1414",
ISBN = "979-8-89176-273-2",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T NarrativeNexus at SemEval-2025 Task 10: Entity Framing and Narrative Extraction using BART
%A Siraj, Hareem
%A Chandani, Kushal
%A Sameen, Dua E.
%A Enayat, Ayesha
%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 siraj-etal-2025-narrativenexus
%X 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.
%U https://aclanthology.org/2025.semeval-1.186/
%P 1411-1414
Markdown (Informal)
[NarrativeNexus at SemEval-2025 Task 10: Entity Framing and Narrative Extraction using BART](https://aclanthology.org/2025.semeval-1.186/) (Siraj et al., SemEval 2025)
ACL