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@article{163912,
        author = {Harinieswari V  and Srimathi T and Vaishnavi R  and Aarthi Gopinath },
        title = {Multifaceted System with T5-based Headline Generation and Established Machine Learning Techniques for Fake News Detection and Summarization},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {2874-2878},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163912},
        abstract = {The ever-growing volume of online news presents a double-edged sword: democratized information access alongside challenges like misinformation and information overload. This work introduces a unified system addressing these issues. The system employs a machine learning model for real-time fake news detection using established techniques like stemming and TF-IDF.  Additionally, it incorporates a summarization module utilizing Latent Semantic Analysis (LSA) to condense lengthy articles.  Uniquely, the system integrates a T5-based deep learning model for headline generation, showcasing its potential in news content processing. This multifaceted approach empowers users with a suite of functionalities within a single framework, ultimately fostering a more trustworthy and efficient news experience, paving the way for a future where navigating the news landscape is not just informative, but streamlined and empowering.},
        keywords = {},
        month = {},
        }