MULTI-SOURCE CONTENT SUMMARIZER

Abstract

The Multi-Source Content Summarizer is an intelligent system designed to extract, process, and condense information from multiple content formats such as YouTube videos, PDF documents, web articles, and raw text. In today’s digital era, the rapid growth of information across various platforms makes it difficult for users to consume and understand large volumes of data efficiently. This system addresses the problem by integrating automated content extraction techniques with an AI-based summarization engine to generate concise, meaningful, and structured summaries. The system converts different input formats into a unified text representation and produces outputs including titles, key points, paragraph summaries, and keywords. It also provides customizable summary length, enabling users to control the level of detail in the output. A chatbot feature is included to enhance user interaction by answering queries related to the summarized content. Additionally, the system stores summary history in a database, allowing users to access, manage, and download previously generated summaries. The application is built using lightweight technologies, ensuring fast processing and ease of deployment. The modular architecture supports scalability and future enhancements such as multilingual support and real-time summarization. This system is particularly useful for students, researchers, and professionals who require quick insights from extensive information sources. Overall, the project demonstrates an efficient and practical approach to multi-format content summarization using modern Natural Language Processing and Artificial Intelligence techniques.