Authors :
Naga Tulasi; Shilpa Sree; Siva Kumar; Indira Kumar; Shirish Kumar Gonala; Bharani Kumar Depuru
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/3ykaehf9
Scribd :
https://tinyurl.com/2s49mey6
DOI :
https://doi.org/10.5281/zenodo.10785429
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.
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.
