News Aggregator for Summarization, Recommendation and Categorization

Ahmad Mukhtar Shah, Aaryan, Ananya Pandit, D Saveetha

2025

Abstract

The enormous amount of news content that is readily available online in the modern digital era makes it difficult for people to find accurate and pertinent information fast. This study investigates the design and refinement of an all-inclusive News Aggregator system that incorporates cutting-edge summarisation and suggestion methods. By integrating cutting-edge algorithms for news summarisation, user behaviour analysis, and personalised content recommendation, the system is intended to address the fundamental problems of information overload, relevancy, and user engagement. This methodology compares several summarisation algorithms, including state-of-the-art approaches like Transformer-based models and more conventional approaches like TF-IDF and TextRank. This assesses these algorithms using performance metrics like ROUGE scores, which allow us to compare how well they produce succinct and useful summaries. In addition, this integrates recommendation algorithms based on machine learning, which use user interaction data to generate customised news feeds that improve user happiness and engagement. The study elucidates the merits and demerits of every approach, providing valuable perspectives on their pragmatic implementation in the news aggregation domain. This provides innovative ways to boost the effectiveness and precision of current algorithms, which will further personalised and effective news consumption. These results show how cutting-edge AI-driven recommendation and summarisation systems may be integrated to handle the issues of information overload, timeliness, and relevance while producing a user-centric news experience. This research provides a framework for the next generation of intelligent news aggregation systems, enabling a more informed and involved society by bridging the gap between user needs and the ever-expanding expanse of digital content.

Paper Citation

in Harvard Style

Mukhtar Shah A., Aaryan., Pandit A. and Saveetha D. (2025). News Aggregator for Summarization, Recommendation and Categorization. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 529-537. DOI: 10.5220/0013596300004664

in Bibtex Style

@conference{incoft25,
author={Ahmad Mukhtar Shah and Aaryan and Ananya Pandit and D Saveetha},
title={News Aggregator for Summarization, Recommendation and Categorization},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={529-537},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013596300004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - News Aggregator for Summarization, Recommendation and Categorization
SN - 978-989-758-763-4
AU - Mukhtar Shah A.
AU - Aaryan.
AU - Pandit A.
AU - Saveetha D.
PY - 2025
SP - 529
EP - 537
DO - 10.5220/0013596300004664
PB - SciTePress