A Multi-Stage Prompt Framework for High-Quality News Summarization with Large Language Models

News summarization is a critical task in natural language processing (NLP) due to the increasing volume of information available online. Traditional extractive summarization methods often fail to capture the nuanced and contextual nature of news content, leading to a growing interest in using large language models (LLMs) like GPT-4 for more sophisticated, abstractive summarization tasks. However, LLMs face challenges in maintaining factual consistency and accurately reflecting the core content of news articles. This research addresses these challenges by proposing a novel prompt engineering method designed to guide LLMs, specifically GPT-4, in generating high-quality news summaries. Our approach utilizes a multi-stage prompt framework that ensures comprehensive coverage of essential details and incorporates an iterative refinement process to improve summary coherence and relevance. To enhance factual accuracy, we include built-in validation mechanisms using entailment-based metrics and question-answering techniques. Experiments conducted on a newly collected dataset of diverse news articles demonstrate the effectiveness of our approach, showing significant improvements in summary quality, coherence, and factual accuracy