Automated text document summarization plays a vital role in extracting key information from large volumes of textual data, aiding in information retrieval, document understanding, and decision-making processes. This paper presents a comprehensive survey and comparative analysis of various automated text document summarization tools. Through a systematic review of state-of-the-art summarization techniques, including extractive, abstractive, and hybrid approaches, this study evaluates the strengths, weaknesses, and performance characteristics of different summarization tools. Key features such as summarization accuracy, coherence, scalability, language support, and usability are assessed to provide insights into the suitability of each tool for different applications and user requirements. Additionally, this paper discusses emerging trends, challenges, and future directions in the field of automated text document summarization, aiming to inform researchers, practitioners, and developers about the current landscape and potential advancements in this critical area of natural language processing.