A Comprehensive Survey on Automated Resume Screening System Using NLP

References

[1] M. Saatçı, R. Kaya, and R. Ünlü, “Resume Screening with Natural Language Processing (NLP),” Alphanumeric, vol. 12, no. 2, pp. 121–140, Dec. 2024.

[2] S. A. Alsaif et al., “NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters,” Big Data and Cognitive Computing, vol. 6, no. 4, p. 147, Dec. 2022.

[3] A. Pimpalkar et al., “Job Applications Selection and Identification: Study of Resumes with Natural Language Processing and Machine Learning,” in Proc. IEEE Int’l Students’ Conf. Electrical, Electronics and Comp. Science (SCEECS), Bhopal, India, 2023, pp. 1–5.

[4] A. K. Sinha, M. A. K. Akhtar, and A. Kumar, “Resume Screening Using Natural Language Processing and Machine Learning: A Systematic Review,” in Adv. Intell. Syst. Comput., vol. 1311, 2021, pp. 207–214.

[5] Y.-C. Chou and H.-Y. Yu, “Based on the Application of AI Technology in Resume Analysis and Job Recommendation,” in Proc. 2020 IEEE (ICCEM), 2020, pp. 291–296.

[6] A. Sharma, S. Singhal, and D. Ajudia, “Intelligent Recruitment System Using NLP,” in Proc. Int’l Conf. AI & Mach. Vision (AIMV), 2021, pp. 1–5.

[7] M. Das, B. Sahoo, and M. Pandey, “A review on text analytics process with a CV parser model,” in Proc. 3rd Int’l Conf. Convergence Technology (I2CT), 2018.

[8] M. Lalitha et al., “Applicant Screening System Using NLP,” in Proc. 2023 Int’l Conf. Innovative Data Communication Tech. & App. (ICIDCA), 2023, pp. 379–383.

[9] H. Sajid et al., “Resume Parsing Framework for E-recruitment,” in Proc. 2022 16th Int’l Conf. Ubiquitous Info. Management and Commun. (IMCOM), 2022, pp. 1–8.

[10] T. M. Harsha et al., “Automated Resume Screener using Natural Language Processing (NLP),” in Proc. 2022 6th (ICOEI), 2022, pp. 1772–1777.

[11] C. Gan, Q. Zhang, and T. Mori, “Application of LLM Agents in Recruitment: A Novel Framework for Automated Resume Screening,” J. Information Processing, vol. 32, no. 881–893, 2024.

[12] S. Chou, “Explainable Person–Job Recommendations: Challenges and Approaches,” IJCS (2023).

[13] A. Ali, N. Mughal, Z. H. Khan, J. Ahmed, and G. Mujtaba, “Resume Classification System using Natural Language Processing and Machine Learning Techniques,” Mehran Univ. Res. J. Eng. Technol., vol. 41, no. 1, pp. 65–79, Jan. 2022.

[14] S. Bharadwaj et al., “Resume Screening using NLP and LSTM,” in Proc. 2022 Int’l Conf. Inventive Computation Tech. (ICICT), 2022, pp. 238–241.

[15] C. Daryani et al., “An Automated Resume Screening System Using Natural Language Processing and Similarity,” Ethics & Info. Technol., Jan. 2020, doi:10.26480/ETIT.02.2020.99.

[16] P. Roy, S. Chowdhary, and R. Bhatia, “A Machine Learning approach for automation of Resume Recommendation system,” Procedia Comput. Sci., vol. 167, pp. 2318–2327, 2020.

[17] C. Spoorthi et al., “Automated Resume Classification System Using Ensemble Learning,” in Proc. 2023 9th Int’l Conf. Advanced Comput. & Commun. Syst. (ICACCS), 2023, pp. 1782–1785.

[18] R. Pal, S. Shaikh, S. Satpute, and S. Bhagwat, “Resume Classification using various Machine Learning Algorithms,” ITM Web of Conferences, vol. 44, Art. 3011, 2022.

[19] D. Pant, D. Pokhrel, and P. Poudyal, “Automatic Software Engineering Position Resume Screening using NLP, Word Matching, Character Positioning, and Regex,” in Proc. 2022 5th Int’l Conf. Adv. Systems & Emergent Tech. (ICASAT), 2022, pp. 44–48.

[20] Z. Naveed, B. Nisar, D. M. Saifullah, and J. Iqbal Baig, “Resume Ranking Using Natural Language Processing,” J. Comput. & Intell. Syst., vol. 2, no. 1, pp. 61–66, 2024.

[21] S. Pulavarthi et al., “Improving Resume Screening with NLP and Machine Learning: Addressing Efficiency and Fairness,” Grenze Int. J. Eng. Technol., vol. 11, no. 1, pp. 1901–1908, Jan. 2025.

[22] A. K. Sinha, M. A. K. Akhtar, and M. Kumar, “Automated Resume Parsing and Job Domain Prediction using Machine Learning,” Indian J. Sci. Technol., vol. 16, no. 26, pp. 1967–1974, Jul. 2023.

[23] A. Deshmukh and A. Raut, “Enhanced Resume Screening for Smart Hiring Using Sentence-BERT,” Int. J. Adv. Comput. Sci. Appl., vol. 15, no. 8, pp. 241–249, 2024.

[24] G. Vihari, H. Naidu, P. K. Yeruva, V. V. Pallela, and R. Pandrinki, “A Resume Screening Operation Powered by AI That Utilizes BERT,” Int. J. Eng. Sci. Adv. Technol. (IJESAT), vol. 25, no. 4, pp. 86–92, Apr. 2025.

[25] Y. G. Deepa, A. Sindhu, A. Shruthi, and N. B. Neha, “Automated Resume Parsing: A Review of Techniques, Challenges and Future Directions,” Int. J. Multidiscip. Res. Growth Eval., vol. 6, no. 2, pp. 1065–1069, Mar.–Apr. 2025.

[26] A. V. K. Reddy, K. V. Ratnam, N. N. Narasimha, and C. P. Kalyan, “AI-Driven Resume Parsing and Ranking System: Leveraging NLP and ML for Efficient Recruitment,” in Proc. 2025 Int. Conf. on Innovative Technology in Software Mgmt. (ICITSM), Tiruchengode, India, Apr. 2025, pp. 1–? (EAI, DOI:10.4108/eai.28-4-2025.2357764).

[27] I. Rathi, P. Kolaskar, L. Tangarlu, and M. Mali, “NLP-Powered Resume Matching for Recruitment,” Int. J. Multidiscip. Res., vol. 6, no. 6, pp. 1–8, Nov.–Dec. 2024.

[28] N. Firdaus, B. K. Riasti, and M. A. Safi’ie, “Comparative Study of Transformer-Based Models for Automated Resume Classification,” J. Ilmu Pengetahuan & Teknol. Komput. (JITK), vol. 11, no. 2, 2025.

[29] P. K. Rangarajan, M. Rithani, B. M. Gurusamy, and V. Ramesh, “Empirical Evaluation of Large Language Models in Resume Classification,” in Proc. 2024 4th Int. Conf. on Advances in Elec., Comput., Commun. & Sust. Tech. (ICAECT), Bhilai, India, Jan. 2024, pp. 1–4, doi:10.1109/ICAECT60202.2024.10469472.

[30] S. Vaishampayan et al., “Human and LLM-Based Resume Matching: An Observational Study,” in Findings of the NAACL 2025, Minneapolis, MN, Apr.–May 2025, pp. 4823–4838, 2025.

[31] H. Artajaya, J. Julieta, J. Giancarlos, J. V. Moniaga, and A. Chowanda, “Job Recommendation System based on Resume using Natural Language Processing and Distance-based Algorithm,” in Proc. 2024 IEEE Int. Conf. on (AIMS), Sept. 2024, pp. 1–6, doi:10.1109/AIMS61812.2024.10512474.

[32] G. N. R. (G. Nathan), M. Vimal, S. M. Akkim, T. A. R. (Tharnesh), and P. Vigneshkumar, “AI Resume Analyzer and Job Recommendation System with NLP Integration,” Int. J. Eng. Dev. Res., vol. 14, no. 1, pp. 463–468, Mar. 2026.

[33] S. M. S. Sheikh, P. Adep, N. Aidasani, S. Chavan, and V. Darade, “AI-Powered Resume Ranking System: Enhancing Recruitment Efficiency through NLP,” Int. J. Res. Trends Innov., vol. 10, no. 5, pp. 367–372, May 2025.

[34] M. B. Yazıcı, D. Sabaz, and W. Elmasry, “AI-based Multimodal Resume Ranking Web Application for Large Scale Job Recruitment,” in Proc. 2024 8th Int. Conf. on AI and Data Processing (IDAP), Sept. 2024, doi:10.1109/IDAP64064.2024.10710945.

[35] B. Sahu, S. Mirza, A. Shikalgar, D. Rupnawar, and P. P. Halkarnikar, “AI-Powered Resume Screening: Enhancing Efficiency and Fairness in Recruitment,” Int. J. Novel Res. Develop., vol. 10, no. 6, pp. 592–595, June 2025.

@article{1719089,
      author = {Bhashkar Biswas, Dr. Tariq Siddiqui},
      title = {A Comprehensive Survey on Automated Resume Screening System Using NLP},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {2161-2169},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1719089.pdf},
      abstract = {Automated resume screening and job recommendation systems have emerged as important applications of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) in modern recruitment processes. These systems aim to automate candidate evaluation by extracting relevant information from resumes and matching it with job requirements, thereby reducing manual effort, screening time, and recruitment costs [1], [2], [21]. Recent advancements in NLP techniques, including named entity recognition, semantic similarity computation, transformer-based language models, and Large Language Models (LLMs) have significantly improved the efficiency and accuracy of candidate-job matching [11], [23], [24], [29]. This survey reviews recent developments in automated resume screening and job recommendation systems published between 2018 and 2026. A detailed analysis of representative studies is presented, focusing on their objectives, methodologies, limitations, and research contributions. The survey identifies common challenges such as limited semantic understanding, lack of large-scale benchmark datasets, insufficient fairness evaluation, and dependence on keyword-based matching approaches [1], [4], [8], [10], [35]. Based on these findings, a conceptual research framework is proposed to integrate advanced NLP techniques with fairness-aware candidate ranking mechanisms. The survey provides valuable insights for researchers and practitioners interested in developing intelligent, explainable, and transparent recruitment systems [11], [12], [30].},
      keywords = {Resume Screening, NLP, Job Recommendation, Candidate Ranking, Semantic Matching, Fairness, Recruitment Analytics.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1719089}
  }