@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://preview.aclanthology.org/corrections-2025-08/2025.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."
}