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@article{206897,
        author = {Sonali Waje and Dr. C. M. Raut},
        title = {An Efficient Transformer Based Framework for Marathi Abstractive Summarization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3250-3256},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206897},
        abstract = {Automatic Text Summarization (ATS) is an important task in the field of Natural Language Processing (NLP) that focuses on generating concise and meaningful summaries from large volumes of textual data. With the rapid growth of digital content available in multiple languages, ATS has gained significant attention from researchers in recent years. The ability to automatically produce accurate summaries has numerous practical applications, including news summarization, research article summarization, financial report analysis, and information retrieval. Although substantial progress has been made in English text summarization due to the availability of large-scale datasets and pretrained language models, Marathi and other Indian regional languages have received comparatively less research attention. The Marathi language presents unique challenges for NLP because of its rich morphological structure and complex syntactic characteristics. This study presents a comparative analysis of different transformer-based models for Automatic Text Summarization in Marathi, aiming to evaluate their effectiveness in generating coherent and contextually relevant summaries.},
        keywords = {Abstractive Text summarization (ATS), Transformer Models, IndicBART, mBART, mT5, Marathi language.},
        month = {July},
        }