Paragraph Text Summarizer
Muhammed Souban
OVERVIEW
This project is a paragraph text summarizer that generates concise summaries from user input text. It was developed using Python and natural language processing techniques with the NLTK library. The system performs sentence tokenization stopword removal and word frequency based scoring to identify the most important sentences in a paragraph. The summarizer works without using any external APIs and provides quick accurate results through a simple web interface built using Flask.
HighlightS
SKILLS
Python programmingNatural Language Processing NLPText preprocessingSentence tokenizationStopword removalWord frequency analysisExtractive text summarizationNLTK libraryFlask web frameworkBasic HTML form handlingWeb application deploymentProblem solving and logic building
Muhammed Souban
Data Analyst & AI Enthusiast
I am a detail-driven data analyst with a BCom in Computer Application, passionate about transforming data into actionable insights. With expertise in Python, SQL, Power BI, and machine learning, I have developed AI-powered chatbots and NLP-based text summarization tools. My internship at Luminar Technolab strengthened my skills in data science, deep learning, and visualization. I thrive on solving real-world problems through data-driven approaches and intelligent automation, constantly exploring new technologies to improve performance and efficiency.
I am a detail-driven data analyst with a BCom in Computer Application, passionate about transforming data into actionable insights. With expertise in Python, SQL, Power BI, and machine learning, I have developed AI-powered chatbots and NLP-based text summarization tools. My internship at Luminar Technolab strengthened my skills in data science, deep learning, and visualization. I thrive on solving real-world problems through data-driven approaches and intelligent automation, constantly exploring new technologies to improve performance and efficiency.
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