Teaching AI to Summarize Like a Human: AReinforcement Learning Experiment

Abstract

Text summarization is a crucial task in natural language processing (NLP), aiming to distill extensive information into concise and coherent summaries. Traditional summarization methods, including both extractive and abstractive techniques, face challenges in generating summaries that balance brevity and informativeness. This paper explores the application of Reinforce- ment Learning with Human Feedback (RLHF) to address these challenges and enhance the quality of text summarization.We introduce an RLHF-based approach using the FLAN-T5-small model, which integrates human feedback into the reinforcement learning framework to refine summary generation. Our method leverages a dataset from the Hugging Face datasets library, consisting of diverse document-summary pairs. The model is pre-trained on a large corpus and fine-tuned using human feedback, which serves as a reward signal to guide the model towards generating more relevant and coherent summaries.Our experimental results demonstrate that the RLHF-enhanced model significantly outperforms traditional summarization methods. Quantitative evaluations using ROUGE and BLEU metrics reveal substantial improvements in summary quality, with increases of up to 12.5% in ROUGE- 1 and 9.8% in BLEU scores over baseline methods. Qualitative assessments by human evaluators further confirm that the RLHF-based model produces summaries that are more aligned with human expectations in terms of coherence and relevance.This study highlights the potential of RLHF to overcome the limitations of conventional summarization tech- niques, offering a robust framework for generating high-quality summaries across various domains. Future work will explore the scalability of this approach to more complex summarization tasks and the integration of additional feedback mechanisms to further enhance performance.