An NLP-Driven Approach for Automated Transcription and Summarization of YouTube Videos

The burgeoning volume of video content on platforms such as YouTube poses considerable obstacles for effective information retrieval, content accessibility, and user engagement. This manuscript advocates for an NLP-oriented methodology aimed at the automated transcription and summarization of YouTube videos, thereby addressing these obstacles and enhancing video searchability, comprehension, and usability. Our approach amalgamates cutting-edge natural language processing (NLP) methodologies with multimedia processing to furnish a comprehensive solution for the extraction of significant content from videos. The initial component of the system constitutes an automatic speech recognition (ASR) model that transcribes the auditory content of the video into text with a high degree of precision. We integrate domain-specific language models to augment the transcription process, particularly in relation to specialized topics or technical jargon. Subsequently, we implement advanced summarization methodologies-encompassing both extractive and abstractive techniques-to produce succinct, coherent summaries of the video’s content.