Year: 2024
Type: article
Abstract: The increasing usage of educational videos as a learning medium poses a challenge in efficiently extracting key information from lengthy content. The "Educational Video Summarisation" system addresses this challenge by providing automatic summaries of educational videos, enabling quicker comprehension and easier note-taking for both teachers and students. By utilizing Natural Language Processing (NLP) techniques, specifically the BART (Bidirectional and Auto-Regressive Transformers) model, the system converts lengthy video transcripts into concise summaries. The transcripts are fetched using the YouTube Transcript API, which supports both English and Hindi. If only a Hindi transcript is available, it is translated into English using Google Translate. The summarized content is then made available through a user-friendly web interface built with Flask. This system streamlines the learning process by reducing the time spent watching long videos while ensuring the retention of essential information. The "Educational Video Summarisation" tool serves as a practical solution in education, improving both teaching and learning efficiency by delivering concise notes from video content.
Authors: Pragati Fatinge -, Sahil Gatkine -, Rishikumar Sinha -, Riddhi Dongarwar -, Sanika Deshpande -, Sakshi Pensalwar -
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4405308530