An Automated Framework for Smart Lecture-to-Notes Generation and Visual Summarization using Deep Learning

In modern educational environments, manual note-taking during long academic lectures often distracts students from active learning and cognitive retention. To address this challenge, this paper proposes an AI-powered automated system, "Smart Lecture-to-Notes Generator," which seamlessly converts spoken educational audio into concise, structured textual summaries and visual representations. The proposed framework employs an advanced Automatic Speech Recognition (ASR) pipeline to transcribe multi-accented lecture audio into high-accuracy text. Subsequently, state-of-the-art Natural Language Processing (NLP) and Transformer models are leveraged to perform abstractive and extractive text summarization, filtering out redundancies while preserving core semantic meaning. To enhance readability, the system integrates a visual mapping module that automatically structures the summarized content into hierarchical notes and mind maps. Deployed via a user-friendly Streamlit web interface, the system demonstrates high efficiency in text reduction and execution speed. This framework serves as a scalable solution for smart classrooms, significantly reducing preparation time for students and optimizing self-paced digital learning. Index Terms — Natural Language Processing, Speech-to-Text, Automatic Speech Recognition, Text Summarization, Smart Education, Streamlit.