Abstract:
Traditional resume analysing systems rely heavily on keyword matching and rigid formats, which
can lead to qualified candidates being overlooked if they don’t conform to the system’s format.
Additionally, these systems often function as “black boxes,” providing a final score without any
transparency in the decision-making process. This lack of explainability can lead to biases in
candidate evaluations, raising concerns about fairness and accuracy. To address these issues, this
project introduces a more transparent, section-based resume scoring system.
A hybrid model which integrates Named Entity Recognition, transformer neural networks, and a
point-based scoring system was developed. It provides granular evaluations of each resume
section. Using Explainable AI (XAI) techniques, the system provides insight into how each
section's score is calculated, promoting trust and accountability. The approach also allows
recruiters to adjust evaluation criteria, enhancing flexibility
The initial Named Entity Recognition (NER) model showed significant results, with an F1 score
of 86.84%, a recall of 88.24%, and a precision of 85.48%. Similarly, the transformer regression
model demonstrated strong performance, with an R-squared value of 0.896428, a Mean Squared
Error (MSE) of 0.194837 and a Mean Absolute Error (MAE) of 0.312830. These results indicate
the effectiveness of the models and their potential for further improvement
