Developing Telegram bot for Text Summarisation using Cohere API
In today’s fast-paced digital world, information overload poses a major challenge, making efficient text summarization essential for quick and effective decision-making. This research introduces an innovative Telegram bot, powered by the Cohere API, that instantly condenses lengthy documents into clear, concise, and meaningful summaries. Designed for seamless integration within the Telegram platform, the bot offers real-time interaction, providing an intuitive and accessible solution for students, researchers, and professionals who need to extract key insights effortlessly. Built on advanced natural language processing (NLP) models, this bot ensures context-preserving and high-quality summaries while significantly reducing the time required to process large volumes of information. This study explores the technical architecture, including API integration, message handling, and summarization logic, ensuring an efficient and user-friendly experience. The bot is designed to work with simple text inputs, making it a hassle-free solution for diverse users. A critical aspect of this research is the performance evaluation of the bot, measuring factors such as accuracy, response time, and user satisfaction. Extensive testing and user feedback highlight its effectiveness in various applications, from academic research and corporate settings to general knowledge consumption. Results demonstrate that automated summarization not only enhances comprehension but also improves productivity by reducing cognitive load. By bridging AI-powered text summarization with everyday communication tools, this paper represents a significant step forward in AI-driven content processing. It showcases the transformative potential of NLP in revolutionizing how users interact with information, paving the way for the future of intelligent, automated knowledge extraction.
Authors
- Maryam Usman Ahmed (ORCID: https://orcid.org/0000-0002-4061-0183)
- S. Indhumathi
- P. Sravan Kumar
- Sindhuja Jamboth
- Harjeet Gudde
Publication Details
- Published
- 2025-05-21
- DOI
- https://doi.org/10.1109/icaiss61471.2025.11041990
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00