Advancements in Automated Resume Assessment: A Comprehensive Survey

Abstract

This comprehensive survey paper delves into the cutting-edge advancements in Automatic Resume Parsing and Ranking Systems, offering an in-depth exploration of methodologies such as Word Embedding, K Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), CNN-Long Short-Term Memory (CNN-LSTM), Ensemble methods, and the strategic integration of Active Questioning (ARQ) techniques. The study emphasizes the adaptability of these methods by acknowledging the importance of hybrid models that tailor the choice of methodology based on the characteristics of the given database. For instance, the paper highlights the suitability of the KNN method for smaller databases, emphasizing its efficiency in such contexts, while advocating for the preference of CNN-LSTM methodology in larger databases to harness its scalability and robustness. The use of ARQ techniques further enriches the evaluation process, employing insightful questions derived from key topics extracted from resumes to not only enhance ranking precision but also identify potential discrepancies or fabrications, thereby fortifying the overall reliability and integrity of the automated resume assessment systems. This nuanced approach aims to optimize the performance of resume parsing and ranking systems across diverse scenarios, offering valuable insights for researchers, practitioners, and industry stakeholders seeking to develop tailored solutions that align with the dynamic nature of candidate profiles and job markets