International Journal of Science, Engineering and TechnologyAuthors: Bhakti Waghmare, Sharon Mavelil, Dr. Jasbir Kaur, Assistant Professor Ifrah Kampoo, Assistant Professor Mansi Rajapurkar
Abstract: With the increasing classic of digital streaming services, video content is a common source for people to learn and transfer knowledge. With the rise of educational videos, podcasts and tutorials, knowledge has become easier to access; however, due to their extended length, learning via long-form video can often be time-consuming and inefficient. Therefore, this research addresses the redundancy and inefficiency of long-form videos by creating an automated system to generate short, concise summaries from video transcripts, thus reducing both length and associated learning time. The proposed system uses Automatic Speech Recognition (ASR) to convert audio to text from a video source and employs the BART model along with a hierarchical chunking approach to generate coherent and meaningful short summaries. Addi-tionally, because transformer-based models have a maximum context length, the system also executes iterative summarization to handle long transcripts effectively. Moreover, for ease of use, the functionality for cross-platform translation is included in the system and supports both high-resource and low-resource languages. Finally, the overall framework architecture is modu-lar in nature, leveraging FastAPI for the back end, while lever-aging Flutter for the front-end mobile app ensuring scalability and user friendliness.
