Intelligent Research Summarization: Enhancing Academic Productivity through AI for a More Sustainable Future
AI summarization; FAISS; Google Gemini AI; LangChain; Natural Language Processing (NLP); Text Embeddings
The efficiency of researchers is often hindered by the enormous volume of academic research, which hinders the extraction of useful insights in a timely manner. This paper proposes an extension for summarizing research papers using artificial intelligence to ease the process through automated concise summarization. Our system leverages cutting-edge natural language processing tools, including PyPDF2 for text extraction, LangChains text chunking method, FAISS for similarity-based information retrieval, and Googles Gemini AI for high-quality summarization. We evaluated the performance of the model using a variety of metrics, including Rouge scores in conjunction with human judgments. Experimental results show that the suggested approach significantly improves research efficiency, reducing reading time by 60% without compromising high accuracy rates. 2025 IEEE.
Priya Dharshini, G.; Ramasamy, Gobi; Sivalingam, Elangovan; Ramachandran, Selvakumar
Proceedings of 2025 International Conference on Emerging Technologies in Computing and Communication, ETCC 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ETCC65847.2025.11108492 https://www.scopus.com/pages/publications/105015628224?origin=resultslist
Priya Dharshini G., Department of Computer Science, CHRIST University, Bangalore, India; Ramasamy G., Department of Computer Science, CHRIST University, Bangalore, India; Sivalingam E., eBay, United States; Ramachandran S., Oxford University, United Kingdom