Text summarization plays a crucial role in distilling essential information from large volumes of text. In this study, we propose a hybrid approach to text summarization that synergizes semantic role labeling (SRL) techniques. By integrating SRL, which identifies the roles of words and phrases in a sentence, with traditional text summarization methods, we aim to enhance the quality and coherence of generated summaries. Our approach leverages the rich semantic information provided by SRL to extract salient content and improve the overall effectiveness of the summarization process. Through experimental evaluation and comparison with existing methods, we demonstrate the efficacy of our hybrid approach in generating concise and informative summaries across various text genres and domains.