Text_Summarization
Project Overview
Text summarization is a core task in Natural Language Processing (NLP) that focuses on automatically generating a concise and coherent summary of a longer text document—while preserving its key information and meaning. It is especially useful for handling the information overload we face today, whether in news articles, research papers, customer reviews, or legal documents.
Tools & Technologies Used
Resources
NLTK Resources : Punkt AND Stopwords
Methodology / Process
- Extraction of text from the PDF.
- Preprocessing the Text.
- Summarization using TextRank.
- Summarization using BERT (BART model).
- Combining the pipeline.
- Testing the pipeline.
Extraction of text from the PDF
Preprocessing the Text
Summarization using TextRank
Summarization using BERT (BART model)
Combining the pipeline.
Testing the pipeline
🔐 Full Project Files – Locked Content
This download contains:
✔️ Full Jupyter Notebook
✔️ Cleaned Review Dataset (.csv)
✔️ Trained Naive Bayes Model (.pkl)
Get lifetime access by unlocking the content below.