Implementation on Document Summarizer with Realtime News Integration
Prof. Suvarna Sujit Wakchaure
suvarna.jondhale@pravara.in
Professor, Department of Computer Engineering
Sir Visvesvaraya Institute
Of Technology, Nashik
Mr. Darshan Mangesh Jadhav,
darshanmjadhav05@gmail.com,
Department of Computer Engineering
Sir Visvesvaraya Institute
Of Technology, Nashik
Mr. Dhanraj Rajendra Dingar,
dhanrajdingar@gmail.com
Department of Computer Engineering
Sir Visvesvaraya Institute
Of Technology, Nashik
Miss. Neha Sushil Gupta,
neha.gupta012004@gmail.com,
Department of Computer Engineering
Sir Visvesvaraya Institute
Of Technology, Nashik
Mr. Rehan Abid Tamboli,
reeehant@gmail.com,
Department of Computer Engineering
Sir Visvesvaraya Institute
Of Technology, Nashik
Abstract: In today’s fast-paced digital era, individuals encounter vast amounts of information through documents, articles, and reports. Reading and comprehending all this content can be time-consuming and challenging. To address this issue, this project presents an Intelligent Document Summarization System that automatically condenses lengthy documents while preserving their key ideas and essential meaning. The system supports multiple file formats, including PDF, TXT, and DOCX, and employs advanced text extraction and summarization techniques to produce concise and coherent summaries. It also offers multilingual support for English, Hindi, and Marathi, enhancing accessibility for diverse users. Additionally, the system integrates real-time related news retrieval, enabling users to stay informed on topics relevant to their documents. Users can easily copy, download, or share summaries, and with secure login and authentication, they can save and manage their summary history. Overall, this project delivers a smart, efficient, and user-friendly solution for managing and understanding large volumes of information, ultimately helping users save time and enhance productivity.
Key Words: Intelligent Document Summarization, Text Summarization, Automatic Summarization,Multi-format Document Support (PDF, TXT, DOCX), Text Extraction, Natural Language Processing (NLP).
