Abstract

VIDMIND is an AI-powered system designed to automate the summarization, analysis, and extraction of key information from YouTube video content. By leveraging natural language processing (NLP) techniques, such as text embeddings and transcript analysis, the system allows users to efficiently comprehend video content without the need for manual viewing. The project addresses the challenge of information overload by providing concise video summaries and actionable insights through a user-friendly interface.

Methodology

VIDMIND employs the following approach to achieve its objectives:

1. Data Collection: Extracts video transcripts using the YouTube Data API.
2. Embedding Generation: Converts transcripts into vector embeddings using OpenAI API and Gemini API.
3. Data Storage: Stores embeddings in a vector database ( AstraDb, Mongodb) for efficient querying.
4. Summarization: Applies NLP techniques to generate concise summaries from processed embeddings.
5. Interface Design: Presents the summaries and insights in a web-based interface built with React for intuitive UI.

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Results

1. Efficiency: Users can extract actionable insights from hours-long videos in minutes.
2. Accuracy: Summaries generated by VIDMIND have shown high relevance in capturing key information from transcripts.
3. Scalability: The system is extendable to include visual metadata extraction for a more comprehensive analysis.
4. Impact: The platform significantly reduces manual effort, enabling professionals in various domains to make data-driven decisions faster.