# Data-driven HR - Résumé Analysis Based on Natural Language Processing and Machine Learning

**Authors:** Tim Zimmermann, Leo Kotschenreuther, Karsten Schmidt  
**arXiv:** [1606.05611](https://arxiv.org/abs/1606.05611)  
**Date:** 2016-06-17  
**Summary page:** https://www.summarizepaper.com/en/paper/1606.05611v2/

## Key points

- Recruiters spend less than a minute reviewing each resume
- Quick scan focuses on keywords, making fair selection challenging
- Data-driven approach introduced to process resumes automatically
- Machine Learning and Natural Language Processing used for efficient information extraction
- Ranking score generated based on education, work experience, and skills to identify promising candidates
- Prototype application demonstrates how the approach enhances recruiter productivity
- Application allows filtering and ranking of candidates based on job descriptions
- Ranking system provides deeper insights into candidate profiles for recruiters
- Tool aims to improve hiring process by providing unbiased decision support
- Importance of tailoring resumes to specific job requirements emphasized
- Need for tools enabling objective evaluation and ranking of candidates highlighted

## Summary

Recruiters typically spend less than a minute reviewing each resume to determine if a candidate should proceed in the recruitment process. This quick scan often focuses on keywords, making it challenging to ensure a fair selection process. To address this issue, a data-driven approach is introduced in this paper, showcasing how resumes can be processed automatically. By leveraging Machine Learning and Natural Language Processing, all necessary information from resumes can be extracted efficiently. Once the information is gathered, a ranking score is generated based on factors such as education, work experience, and skills. This score helps recruiters identify promising candidates more effectively. The paper also presents a prototype application that demonstrates how this innovative approach can enhance recruiter productivity. The application allows recruiters to filter and rank candidates based on predefined job descriptions. With the aid of the ranking system, recruiters gain deeper insights into candidate profiles and can validate why certain candidates were ranked higher. Ultimately, this tool aims to improve the hiring process by providing unbiased decision support for recruiters. Furthermore, the research prototype highlights the importance of tailoring resumes to specific job requirements and emphasizes the need for tools that enable objective evaluation and ranking of candidates. By utilizing such tools, recruiters can not only find the best match for a position but also efficiently process a larger pool of potential candidates.