Publication Date: 2024/03/06
Abstract: In the dynamic realm of the global news industry, media outlets employ extensive efforts to curate and present data gathered from diverse online and offline sources. The intricate procedure involves not only the collection of news but also its condensation into succinct and informative summaries, often accompanied by a meticulous classification based on significance. However, this multifaceted undertaking is inherently time- consuming, demanding substantial manual input. Enter the realm of AI, a transformative force poised to revolutionise the landscape of news processing. LLM technology, such as advanced models developed through tailored ML, offers a compelling solution to the problems involved in news extraction and creation of summaries. These algorithms, which are designed to handle unimaginable data, can swiftly and accurately analyse manually and programmatically collected data from a myriad of resources, significantly reducing the burden of manual efforts. The implementation of such AI-driven solutions not only expedites the news processing workflow but also introduces a layer of sophistication by discerning and categorising the importance of news items with unparalleled accuracy. Moreover, the integration of Python libraries designed for online data scraping enhances the efficiency of the entire system. These libraries organise the process of gathering information from various sources, offering a seamless flow of data to the LLMs. The combined synergy of this technology not only saves valuable time but also elevates the quality of news reporting, ensuring that the audience receives well- curated, relevant, and timely information. In essence, the marriage of AI, LLM models, and Python libraries forms a powerful triumvirate, offering an intelligent and automated solution to the issues prevalent in news extraction and summarising it. As the media landscape continues to evolve, these advancements represent a pivotal step towards a future where information dissemination is not only efficient but also nuanced and tailored to the diverse needs of a global audience. In this paper, our aim is to implement a model that automatically scrapes the content and summarises it, leveraging advanced scraping python packages and LLM models. Through the seamless overlapping of these technologies, we strive to revolutionise NEWS processing, ensuring swift, accurate, and insightful summarization of diverse resources.
Keywords: News Summarisation, News Scraping, Large Language Modelling, Natural Language Processing, Artificial Intelligence, Text Extraction, Web Scraping, Generative AI.
DOI: https://doi.org/10.5281/zenodo.10785429
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB1525.pdf
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