CV Summary and Professional Recommendations Using RAG and NLP
DOI:
https://doi.org/10.5281/zenodo.17645956Keywords:
Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Keyword Extraction, Job Matching, Semantic Search, Transformer-Based LLM, FAISSAbstract
Job searching can be a very tedious affair as one has to tailor-make resumes to fit every job posting. This article provides an AI-driven approach that will cut down the fuss of making resumes, choosing keywords, and matching them precisely with job postings through RAG and NLP. The system merges a transformer-based LLM with semantic search and vector embeddings to quickly identify the roles, qualifications, experience, and skills that users highlight in their extracts. Keyword extraction also aligns with job market trends to increase application success rates. The job matching module uses FAISS-based semantic search, ranking opportunities by relevance and skill match. Mass-scale experimentation with different sets of resume and job posting data confirms the effectiveness of the system with an astonishing 92% accuracy in job matching and skill extraction. By bridging the gap between recruiters and job candidates, the process streamlines candidate profiling, making the hiring process more accurate, precise, and data-driven.
Downloads
References
M. Ahmed Zeyad, & A. Biradar. (2024). Advancements in the efficacy of flan-T5 for abstractive text summarization: A multi-dataset evaluation using ROUGE and BERT score. International Conference on Advancements in Power, Communication and Intelligent Systems (APCI), pp. 1-5. DOI: 10.1109/APCI61480.2024.10616418.
D. Singh. (2024). Legal documents text analysis using natural language processing (NLP). 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), pp. 1302-1307. DOI: 10.1109/ICSSAS64001.2024.10760929.
S. Jugran, A. Kumar, B. S. Tyagi, & V. Anand. (2021). Extractive automatic text summarization using SpaCy in Python NLP.0 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 582-585. DOI: 10.1109/ICACITE51222.2021.9404712.
S.Bhardwaj, & J. Ghayathri. (2024). Domain specific text summarization using latent semantic analysis (LSA) and performing visualization using PylDAVIS. 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), pp. 1-9, DOI: 10.1109/IC- SCAI61790.2024.10866589.
O. B. Mercan, S. N. Cavsak, A. Deliahmetoglu, & S. Tanberk. (2023). Abstractive text summarization for resumes with cutting edge NLP transformers and LSTM. Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1-6. DOI: 10.1109/ASYU58738.2023.10296563.
M. M, G. Sunil, K. AL-Attabi, I. H. Mohammed, & P. R. Buvaneswari. (2023). An abstractive text summarization using decoder attention with pointer network. International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), pp. 1-6. DOI: 10.1109/AIKIIE60097.2023.10389942.
E. S. L. Tsoumou, L. Lai, S. Yang, & M. L. Varus. (2016). An extractive multi-document summarization technique based on fuzzy logic approach. International Conference on Network and Information Systems for Computers (ICNISC), pp. 346-351. DOI: 10.1109/ICNISC.2016.081.
R. Dumne, N. L. Gavankar, M. M. Bokare, & V. N. Waghmare. (2024). Automatic text summarization using text rank algorithm. 3rd International Conference for Advancement in Technology (ICONAT), pp. 1-6. DOI: 10.1109/ICONAT61936.2024.10775241.
N. Zade, G. Mate, K. Kishor, N. Rane, & M. Jete. (2024). NLP based automated text summarization and translation: A comprehensive analysis. 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS), pp. 528-531. DOI: 10.1109/ICSCSS60660.2024.10624907.
Ray, A. Mishra, B. Sahoo, & H. K. Tripathy. (2024). Comparative analysis of extractive summarization using NLP. International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC), pp. 1-5. DOI: 10.1109/ICEC59683.2024.10837027
S. Sharma, & M. L. Saini. (2022). Analyzing the need for video summarization for online classes conducted during covid-19 lockdown. Lect. Notes Electr. Eng., 907, pp. 333–342. DOI: 10.1007/978-981-19-4687-5_25.
S. Kulshrestha, & M. L. Saini. (2020). Study for the prediction of e-commerce business market growth using machine learning algorithm. 5th IEEE Int. Conf. Recent Adv. Innov. Eng. ICRAIE 2020 – Proceeding. DOI: 10.1109/ICRAIE51050.2020.9358275.
Y. Singh, M. Saini, & Savita. (2023). Impact and performance analysis of various activation functions for classification problems. Proc. IEEE InC4 2023 - 2023 IEEE Int. Conf. Contemp. Comput. Commun. DOI: 10.1109/InC457730.2023.10263129.
K. Lal, & M. L. Saini. (2023). A study on deep fake identification techniques using deep learning. AIP Conf. Proc., 2782. DOI: 10.1063/5.0154828
J. Sarmah, M. L. Saini, A. Kumar, & V. Chasta. (2024). Performance analysis of deep CNN, YOLO, and LeNet for handwritten digit classification. Lect. Notes Networks Syst., 844, pp. 215–227. DOI: 10.1007/978-981-99-8479-4_16.
M. L. Saini, R. S. Telikicharla, Mahadev, & D. C. Sati. (2024). Handwritten english script recognition system using CNN and LSTM. Proc. InC4 2024 - 2024 IEEE Int. Conf. Contemp. Comput. Commun. DOI: 10.1109/InC460750.2024.10649099.
B. Mulakala, M. L. Saini, A. Singh, V. Bhukya, & A. Mukhopadhyay. (2024). Adaptive multi-fidelity hyperparameter optimization in large language models. In: 8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions. DOI: 10.1109/CSITSS64042.2024.10816794.
C. Sasidhar, M. L. Saini, M. Charan, A. V. Shivanand, & V. M. Shrimal. (2024). Image caption generator using LSTM. Proc. - 4th Int. Conf. Technol. Adv. Comput. Sci., pp. 1781–1786. DOI: 10.1109/ICTACS62700.2024.10841294.
D. Gupta, M. L. Saini, S. P. K. Mygapula, S. Maji, & V. Prabhas. (2024). Generating realistic images through GAN. In: Proceedings - 4th International Conference on Technological Advancements in Computational Sciences, pp. 1378–1382. DOI: 10.1109/ICTACS62700.2024.10841324.
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Utsha Sarker, Archy Biswas, Saurabh, Lalit Vaishnav, Myla Vizwal Rathod
This work is licensed under a Creative Commons Attribution 4.0 International License.
Research Articles in 'International Journal of Engineering and Management Research' are Open Access articles published under the Creative Commons CC BY License Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/. This license allows you to share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially.
