From Manual Review to AI Automation: An NLP-Powered System for Efficient CV Processing in Academic Admissions

Authors

DOI:

https://doi.org/10.62486/latia2025315

Keywords:

CV Summarization, NLP, Named Entity Recognition, Extractive Summarization, Abstractive Summarization, Candidate Ranking, Higher Education

Abstract

Manual screening of thousands of admissions of master's program applications at Hassan II University of Casablanca is a time and labor-intensive task. Towards this challenge, we designed a machine-based solution utilizing Natural Language Processing (NLP) for summarization and CV ranking on a large set of CVs. Our solution relies on pre-trained spaCy and Hugging Face Transformers-based Named Entity Recognition (NER) models for the retrieval of information such as education, experience, and skills. We then incorporated extractive summarization by using BERT-based models for the selection of the most informative sentences and then the abstractive summarization by utilizing advanced language models such as LLAMA for the summaries to be coherent and easy. We verified our system by conducting a case study of the master's program of Big Data and Data Science by running a set of 2,325 CVs. The model gave very good results like a 72,67 % ROUGE-1 Recall, 74,32 % ROUGE-2 Recall, 73,15 % ROUGE-1 Precision, 57,28 % ROUGE-2 Precision, and 82% Named Entity Recognition (NER) Precision. The system processed a CV on average in 3,84 seconds. We also integrated a conversation bot (chatbot) that allows admissions teams to search the CVs uploaded in real time for improved decision-making effectiveness and significantly decreasing the administrative burden. The promise of NLP-driven automation stands out from this research as a scalable as well as efficient method of screening numerous applicants.

References

Enhanced Resume Screening for Smart Hiring using Sentence-Bidirectional Encoder Representations from Transformers (S-BERT). 2024.

A Novel Pipeline for Improving Optical Character Recognition through Post-processing Using Natural Language Processing. 2023.

Abstractive Text Summarization for Resumes with Cutting Edge NLP Transformers and LSTM. 2023.

Analyzing CV/Resume Using Natural Language Processing and Machine Learning. 2022.

Automatic Software Engineering Position Resume Screening Using Natural Language Processing, Word Matching, Character Positioning, and Regex. 2022.

NLP-Based Automatic Summarization Using Bidirectional Encoder Representations from Transformers–Long Short-Term Memory Hybrid Model: Enhancing Text Compression. 2024.

Leveraging NLP and AI for Advanced Chatbot Automation in Mobile and Web Applications. 2021.

Resume Ranking Using Natural Language Processing. 2024.

Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening. 2024.

Auer C, Lysak M, Nassar A, Dolfi M, Livathinos N, Vagenas P, et al. Docling Technical Report. arXiv [Preprint]. 2024 Aug 18 [cited 2025 May 6]; Available from: https://arxiv.org/abs/2408.09869

Downloads

Published

2025-05-20

Issue

Section

Original

How to Cite

1.
Chafiq N, Ghazouani M, El Gounidi R. From Manual Review to AI Automation: An NLP-Powered System for Efficient CV Processing in Academic Admissions. LatIA [Internet]. 2025 May 20 [cited 2025 Jun. 17];3:315. Available from: https://latia.ageditor.uy/index.php/latia/article/view/315