@inproceedings{premnath-etal-2025-techssn,
title = "{TECHSSN} at {S}em{E}val-2025 Task 10: A Comparative Analysis of Transformer Models for Dominant Narrative-Based News Summarization",
author = "Premnath, Pooja and
Yenumulapalli, Venkatasai Ojus and
Mohankumar, Parthiban and
Sivanaiah, Rajalakshmi and
S, Angel Deborah",
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-1.286/",
pages = "2205--2212",
ISBN = "979-8-89176-273-2",
abstract = "This paper presents an approach to Task 10 of SemEval 2025, which focuses on summarizing English news articles using a given dominant narrative. The dataset comprises news articles on the Russia-Ukraine war and climate change, introducing challenges related to bias, information compression, and contextual coherence. Transformer-based models, specifically BART variants, are utilized to generate concise and coherent summaries. Our team TechSSN, achieved 4th place on the official test leaderboard with a BERTScore of 0.74203, employing the DistilBART-CNN-12-6 model."
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<abstract>This paper presents an approach to Task 10 of SemEval 2025, which focuses on summarizing English news articles using a given dominant narrative. The dataset comprises news articles on the Russia-Ukraine war and climate change, introducing challenges related to bias, information compression, and contextual coherence. Transformer-based models, specifically BART variants, are utilized to generate concise and coherent summaries. Our team TechSSN, achieved 4th place on the official test leaderboard with a BERTScore of 0.74203, employing the DistilBART-CNN-12-6 model.</abstract>
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%0 Conference Proceedings
%T TECHSSN at SemEval-2025 Task 10: A Comparative Analysis of Transformer Models for Dominant Narrative-Based News Summarization
%A Premnath, Pooja
%A Yenumulapalli, Venkatasai Ojus
%A Mohankumar, Parthiban
%A Sivanaiah, Rajalakshmi
%A S, Angel Deborah
%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 premnath-etal-2025-techssn
%X This paper presents an approach to Task 10 of SemEval 2025, which focuses on summarizing English news articles using a given dominant narrative. The dataset comprises news articles on the Russia-Ukraine war and climate change, introducing challenges related to bias, information compression, and contextual coherence. Transformer-based models, specifically BART variants, are utilized to generate concise and coherent summaries. Our team TechSSN, achieved 4th place on the official test leaderboard with a BERTScore of 0.74203, employing the DistilBART-CNN-12-6 model.
%U https://aclanthology.org/2025.semeval-1.286/
%P 2205-2212
Markdown (Informal)
[TECHSSN at SemEval-2025 Task 10: A Comparative Analysis of Transformer Models for Dominant Narrative-Based News Summarization](https://aclanthology.org/2025.semeval-1.286/) (Premnath et al., SemEval 2025)
ACL