Year: 2025
Type: article
Abstract: - Information overload has resulted from the influx of digital news, making it difficult for readers to identify essential details. This paper presents a news summarization system that combines ethical analysis and key insight to ensure fairness, credibility, and relevance. The system filters out misleading content, highlights crucial points and provides a structured understanding of news topics. The paper details the system's architecture, implementation, evaluation metrics, and ethical considerations. According to the results, the proposed model enhances news consumption by delivering concise, unbiased, and informative summaries. Key Words: GPT-3, NLP (Natural Language Processing), Multi-Stage Attention Mechanisms, OLLAMA, VADER Sentiment Analysis, Summarizer
DOI: 10.55041/ijsrem41590
Author: Y. Hemalatha
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407593355