Assessing the Impact of Prompt Strategies on Text Summarization with Large Language Models

The advent of large language models (LLMs) has significantly advanced the field of text summarization, enabling the generation of coherent and contextually accurate summaries. This paper introduces a comprehensive framework for evaluating the performance of state-of-the-art LLMs in text summarization, with a particular focus on the impact of various prompt strategies, including zero-shot, one-shot, and few-shot learning. Our framework systematically examines how these prompting techniques influence summarization quality across diverse datasets, namely CNN/Daily Mail, XSum, TAC08, and TAC09. To provide a robust evaluation, we employ a range of intrinsic metrics such as ROUGE, BLEU, and BERTScore. These metrics allow us to quantify the quality of the generated summaries in terms of precision, recall, and semantic similarity. We evaluated three prominent LLMs: GPT-3, GPT-4, and LLaMA, each configured to optimize summarization performance under different prompting strategies. Our results reveal significant variations in performance depending on the chosen prompting strategy, highlighting the strengths and limitations of each approach. Furthermore, this study provides insights into the optimal conditions for employing different prompt strategies, offering practical guidelines for researchers and practitioners aiming to leverage LLMs for text summarization tasks. By delivering a thorough comparative analysis, we contribute to the understanding of how to maximize the potential of LLMs in generating high-quality summaries, ultimately advancing the field of natural language processing.