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@article{174673,
        author = {K.Sriram and G.Srinidhi and P.Srinika and A.Yuktheswar and S.Mani Kumar and Dr.Sujit Das},
        title = {Unsupervised Topic Modeling and Summarization of Scientific Research Documents},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1092-1100},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174673},
        abstract = {This project creates an automatic tool that finds the main ideas and makes short summaries of science research papers, doing a better job than old ways of reading by hand or using basic programs. Unlike other tools that need lots of human work or can’t change to fit different kinds of studies, our tool does everything on its own using smart methods to pick out key points and shorten papers without needing anyone to label things first. This makes it fast and useful for all sorts of research, like health, tech, or nature studies. It can read many file types—like PDFs, Word files, regular text, and website links—and even pulls out pictures, giving researchers a fuller picture than older tools that only look at words. We tested it, and it works great: it cuts papers down by up to 36% without losing the big ideas, gets high marks for being right (like 0.82 out of 1 on a common test score), and stays very close to the original meaning (up to 0.95). This easy-to-use tool runs on a website, saving researchers time and helping them get clear, useful answers from piles of complicated science papers. It fixes the problems of slow, manual reading or stiff tools by letting scientists spend more time on new ideas instead of digging through long documents.},
        keywords = {manual reading replacement, research efficiency, science research papers, key ideas, smart methods, file types, PDFs, Word, text, links, picture extraction},
        month = {April},
        }