Jayesh Chavan,
Smita Palnitkar,
Ajinkya Kaje,
Chaitanya Bhopnikar,
- Student, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
- Assistant professor, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
- Student, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
- Student, Department of Electronics and Telecommunication, Smt. Kashibai Navale College of Engineering, Savitribai Phule Pune University, Pune, Maharashtra, India
Abstract
‘]
Transcriber Sum is a deep learning-based YouTube transcript summarization tool. It employs advanced machine learning techniques to automatically generate concise summaries of YouTube video transcripts, enabling users to quickly grasp the key content and insights of videos without the need to watch or read the entire transcript. This Transcriber Sum addresses the challenge of providing users with concise and informative summaries of video content by harnessing the power of deep learning techniques. The objectives encompass the creation of advanced models for natural language processing and video content analysis, building a robust framework for processing and summarizing video transcripts, collecting and annotating a diverse dataset of YouTube video transcripts, implementing data preprocessing and augmentation techniques, training and evaluating the model’s performance, developing a user- friendly application for real-time transcript summarization, and continuously refining the model based on user feedback. The methodology involves developing deep learning models for natural language understanding, utilizing advanced video content analysis techniques, and seamlessly integrating these components into an application for effortless YouTube transcript summarization. Performance evaluation is conducted through a combination of simulation using synthetic data and real-world testing with a wide range of YouTube video transcripts. Transcriber Sum aims to empower users with a time-efficient and informative tool for quickly summarizing the wealth of content available on YouTube, ultimately enhancing the overall viewing experience and accessibility of valuable information in the digital age.
Keywords: Transcript summarization, Deep learning, Machine learning techniques, Natural language processing, Real-time, AI/Deep Learning.
[This article belongs to Journal of Artificial Intelligence Research & Advances (joaira)]
Jayesh Chavan, Smita Palnitkar, Ajinkya Kaje, Chaitanya Bhopnikar. Transcript Summarizer of YouTube Videos Using Deep Learning. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):-.
Jayesh Chavan, Smita Palnitkar, Ajinkya Kaje, Chaitanya Bhopnikar. Transcript Summarizer of YouTube Videos Using Deep Learning. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):-. Available from: https://journals.stmjournals.com/joaira/article=2024/view=171718
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Journal of Artificial Intelligence Research & Advances
| Volume | 11 |
| Issue | 03 |
| Received | July 3, 2024 |
| Accepted | September 6, 2024 |
| Published | September 11, 2024 |