An Extensive Survey on Audio-to-Text and Text Summarization for Video Content

This literature review examines evolving trends in audio-to-text and text summarization techniques applied to YouTube video content. With the rapid expansion of online video platforms, the demand for efficient transcription and summarization of multimedia data has grown significantly. This study offers a comprehensive analysis of current methodologies, algorithms, and tools for converting audio content to text, highlighting the intricacies of speech recognition technologies and their applicability in various contexts. Additionally, the survey explores text summarization techniques specifically designed for YouTube videos, including both extractive and abstractive methods. It assesses their effectiveness in condensing lengthy video transcripts into concise and coherent textual representations. Special emphasis is given to NLP (Natural Language Processing) algorithms and ML (Machine Learning) models that aid in extracting essential information while maintaining contextual relevance.