Deepfake-Driven Lecture Summary Generation

The increasing demand for high-quality, user-friendly, and engaging educational materials aligns with the rise of e-learning platforms. In response, a new approach is proposed to enhance the accessibility and engagement of educational content by leveraging deepfake technology. This methodology encompasses data collection, preprocessing, deepfake model training, text-to-speech translation, and content compilation. Ethical considerations, particularly regarding authorization and appropriate educational use, are also addressed. This study introduces an innovative method for delivering instructional content by combining text summarization with deepfake technology. The approach involves creating a simulated deepfake of a lecturer and reproducing their voice through lip-synchronization, and lecture summarization. This dual approach ensures clear and unambiguous content presentation, while the use of deepfake technology enhances engagement by providing a more authentic learning experience. The goal is to leverage cutting-edge technology to revolutionize the production of lecture summary videos, ultimately fostering a more efficient and equitable learning environment. The paper aims to present an integrated framework comprising various modules to offer a new methodology for producing lecture summary videos and streamline the online content generation process.