Multifaceted System with T5-based Headline Generation and Established Machine Learning Techniques for Fake News Detection and Summarization

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Harinieswari V; Srimathi T; Vaishnavi R; Aarthi Gopinath

Multifaceted System with T5-based Headline Generation and Established Machine Learning Techniques for Fake News Detection and Summarization

Authors: Harinieswari V , Srimathi T, Vaishnavi R , Aarthi Gopinath

Unique Paper ID: 163912
Volume: 10
Issue: 11
PageNo: 2874-2878

Keywords: No Keywords Found

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.

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Cite This Article

ISSN: 2349-6002
Volume: 10
Issue: 11
PageNo: 2874-2878

Multifaceted System with T5-based Headline Generation and Established Machine Learning Techniques for Fake News Detection and Summarization

Available:https://ijirt.org/article?manuscript=163912

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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