The document outlines a project on resume and CV summarization using NLP to streamline the recruitment process by converting unstructured resumes into structured data for efficient processing. It describes the challenges faced by companies in handling large volumes of resumes and proposes a solution that uses decision tree algorithms to classify candidates based on qualifications and experience. The application aims to enhance recruiting efficiency, reduce hiring costs, and improve candidate placement across various sectors.

1. Resume and CV
Summarization using NLP

2. Team member
Anjali Asrani 05
Sneha Indulkar 23
Kaif siddiqui 63
Project guide : Mrs. Vidya zope

3. INTRODUCTION
After completing education the next phase that comes in a person’s life is a job. However, there
are lots of people who start working before completing their formal education.
Every organization has to deal with folders together with resumes Going through these resumes
can be a tiring process added to the fact that it is very time consuming.
The structured data extraction process is done by segmenting the entire CV / Resume by
converting it to text. After the conversion to structured data, decision tree algorithm techniques
are used to classify the input into different categories based on qualifications, experience etc.
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4. PROBLEM DEFINITION
Large companies and recruitment agencies receive, process and manage hundreds of resumes from job
applicants. Besides, many people publish their resumes on the web. Dealing with loads of resumes at once can be
time consuming since all they need is a bunch of valuable resumes that represent candidates specialized in fields
the company/agency is looking for. The resumes that one receives in their original format (pdf, docm etc.) are
unstructured. The unstructured format of these resumes , with random templates and fonts make it difficult for
processing. These resumes can be automatically retrieved and processed by a resume information extraction
system. Extracted information such as name, phone / mobile nos., e-mails id., qualification, experience, skill sets
etc. can be stored as structured information in a database.

5. TECHNOLOGIES TO BE USED
1. Software requirement :
i. Jupyter notebook
ii. Colab
2. Technology used
i. NLP
ii. Python
iii. Spacy tool

6. SYSTEM DIAGRAM

7. IMPLEMENTATION
Resume and CV Summarization Using NLP

9. Summarized result

10. The application can be extended further to other domains like Telecom, Healthcare, E-commerce and public
sector jobs
FUTURE SCOPE

11. CONCLUSION
Our application helps the recruiters to screen the resumes more efficiently thereby reducing the cost of
hiring. This will provide a potential candidate to the organization and the candidate will be successfully placed in
an organization which appreciates his/her skill set and ability.

12. Thank you!