Summarization in speech-to-text (STT) AI enhances user experience by distilling essential information from audio content, leveraging automatic speech recognition (ASR) systems and large language models (LLMs) like neural networks. Gladia's innovative approach uses Mistral and the main index to address traditional summarization challenges, such as finite context size and "catastrophic forgetting," by employing techniques like chunking and embedding algorithms. These methods allow for processing infinite contexts and maintaining attention throughout conversations, resulting in precise abstract summaries. Gladia's API offers customizable summarization outputs and emphasizes the importance of prompt engineering in maximizing summary quality. The advancements in summarization technologies have significant implications for the future of STT, enabling more efficient and effective communication extraction.