Ever read an AI-generated transcript and been frustrated by the errors and informal speech? Researchers are tackling this problem with an innovative approach they call "Contextualized Spoken-to-Written Conversion" or CoS2W. Imagine turning a jumbled mess of filler words, repetitions, and grammatical errors into a polished, formal document. That's the goal of CoS2W. This technology aims to improve not just the readability of transcripts but also their usefulness for tasks like translation and summarization. Why is this important? Because current ASR transcripts, while capturing what was said, often lack the clarity needed for efficient communication. This research explores how Large Language Models (LLMs) can be used to bridge this gap. They've created a new dataset, called SWAB, to test how different LLMs perform in this complex task. The results are promising, showing that LLMs like GPT-4 can significantly improve the formality and grammatical accuracy of transcripts. However, challenges remain, particularly with ensuring the converted text stays true to the original meaning, a crucial aspect for accurate information sharing. The future of this technology lies in refining these models, developing better evaluation methods, and expanding the available datasets, ultimately paving the way for seamless, accurate communication from spoken word to written document.