Authors :
Vismaya; Leena Shruthi H. M.
Volume/Issue :
Volume 11 - 2026, Issue 8 - August
Google Scholar :
https://tinyurl.com/ywmsfk5d
DOI :
https://doi.org/10.38124/ijisrt/26aug959
Note : A published paper may take 4-5
working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
ResearchGate.
Abstract :
The quick increase in academic research papers makes it impossible for students, researchers, and academicians
to manage their paper database easily and analyze it properly. It takes a lot of time and effort to read lengthy research
papers, find useful information, generate summary, generate citations and write research notes. In order to solve these
problems, in this paper, we propose a web-based platform called ResearchHub: AI-Powered Research Paper Management
System. This platform integrates research paper management with Artificial Intelligence. The proposed system will allow
the user to upload the research papers in PDF format and will provide features such as AI-based summarization, keyword
extraction, IEEE citation generation, personal note management, and AI-powered chat interface to interact with the
uploaded research documents. The system is developed using MERN stack which consists of MongoDB, Express.js, React.js,
and Node.js. The frontend design of the website is done using Tailwind CSS while the Groq API is used to provide AI-based
functionalities. With the help of intelligent document analysis and central research paper management, ResearchHub saves
a lot of effort and enables users to understand and extract useful information from research papers efficiently. It serves as
a perfect research workspace for students, researchers, and academicians and further could be enhanced with collaborative
research, multilingual analysis, plagiarism detection, mobile compatibility, and personalized research recommendations.
Keywords :
Artificial Intelligence, Research Paper Management, Natural Language Processing, MERN Stack, Document Analysis, AI Summarization, Keyword Extraction, Citation Generation, Research Assistant.
References :
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- J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. 2019 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2019, pp. 4171–4186.
- I. Beltagy, K. Lo, and A. Cohan, “SciBERT: A pretrained language model for scientific text,” in Proc. 2019 Conf. Empirical Methods in Natural Language Processing and 9th Int. Joint Conf. Natural Language Processing, Hong Kong, China, 2019, pp. 3615–3620.
- R. Mihalcea and P. Tarau, “TextRank: Bringing order into texts,” in Proc. 2004 Conf. Empirical Methods in Natural Language Processing, Barcelona, Spain, 2004, pp. 404–411.
- J. Zhang, Y. Zhao, M. Saleh, and P. J. Liu, “PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization,” in Proc. 37th Int. Conf. Machine Learning, vol. 119, 2020, pp. 11328–11339.
- A. Cohan, S. Feldman, I. Beltagy, D. Downey, and D. S. Weld, “SPECTER: Document-level representation learning using citation-informed transformers,” in Proc. 58th Annu. Meeting Association for Computational Linguistics, 2020, pp. 2270–2282.
- P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-T. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474.
- A. Nenkova and K. McKeown, “A survey of text summarization techniques,” in Mining Text Data, C. C. Aggarwal and C. Zhai, Eds. Boston, MA, USA: Springer, 2012, pp. 43–76.
- R. Mihalcea and P. Tarau, “An algorithm for language independent single and multiple document summarization,” in Proc. 43rd Annu. Meeting Association for Computational Linguistics, Ann Arbor, MI, USA, 2005, pp. 25–32.
- N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proc. 2019 Conf. Empirical Methods in Natural Language Processing and 9th Int. Joint Conf. Natural Language Processing, Hong Kong, China, 2019, pp. 3982–3992.
- N. Reimers and I. Gurevych, “Sentence-BERT: Sentence embeddings using Siamese BERT-networks,” in Proc. 2019 Conf. Empirical Methods in Natural Language Processing and 9th Int. Joint Conf. Natural Language Processing, Hong Kong, China, 2019, pp. 3982–3992.
- K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning, “ELECTRA: Pre-training text encoders as discriminators rather than generators,” in Proc. 8th Int. Conf. Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.
- Y. Karpukhin, O. Oguz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W.-T. Yih, “Dense passage retrieval for open-domain question answering,” in Proc. 2020 Conf. Empirical Methods in Natural Language Processing, Online, 2020, pp. 6769–6781.
- M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” arXiv preprint arXiv:2203.05794, 2022.
- A. Cohan, F. Ammar, M. van Zuylen, and F. C. Greene, “Structural scaffolds for citation intent classification in scientific publications,” in Proc. 2019 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2019, pp. 4546–4556.
The quick increase in academic research papers makes it impossible for students, researchers, and academicians
to manage their paper database easily and analyze it properly. It takes a lot of time and effort to read lengthy research
papers, find useful information, generate summary, generate citations and write research notes. In order to solve these
problems, in this paper, we propose a web-based platform called ResearchHub: AI-Powered Research Paper Management
System. This platform integrates research paper management with Artificial Intelligence. The proposed system will allow
the user to upload the research papers in PDF format and will provide features such as AI-based summarization, keyword
extraction, IEEE citation generation, personal note management, and AI-powered chat interface to interact with the
uploaded research documents. The system is developed using MERN stack which consists of MongoDB, Express.js, React.js,
and Node.js. The frontend design of the website is done using Tailwind CSS while the Groq API is used to provide AI-based
functionalities. With the help of intelligent document analysis and central research paper management, ResearchHub saves
a lot of effort and enables users to understand and extract useful information from research papers efficiently. It serves as
a perfect research workspace for students, researchers, and academicians and further could be enhanced with collaborative
research, multilingual analysis, plagiarism detection, mobile compatibility, and personalized research recommendations.
Keywords :
Artificial Intelligence, Research Paper Management, Natural Language Processing, MERN Stack, Document Analysis, AI Summarization, Keyword Extraction, Citation Generation, Research Assistant.