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brief-textsummarizer's Introduction

Brief: Text Summarizer

Problem Statement: Create a tool that can summarize longer texts or articles into shorter, more concise versions using NLP techniques.

This platform will help users quickly grasp the main points and key information in a document without having to read the entire content. It is a web application that can summarize a text or a Wikipedia link. Additionally, we can also compare different methods of summarization. It allows users to input a link or text and grasps the keywords to provide an accurate summary.

Programming language: Python

Libraries:
~ Flask: Flask is a micro web framework written in Python. It is used here to create the web application. Flask provides tools, libraries, and technologies for building web applications.

~ Spacy: SpaCy is an open-source natural language processing (NLP) library for Python. It's used for text processing and summarization in this application.

~ Gensim: Gensim is a topic modeling library for Python. In this case, it's used for text summarization. Note that the summarization module has been deprecated in Gensim 4.x, so if you're using Gensim 4.x, you might encounter issues.

~ NLTK (Natural Language Toolkit): NLTK is a powerful library for working with human language data. It's used here for text summarization.

~ BeautifulSoup: BeautifulSoup is a library for pulling data out of HTML and XML files. It's used for web scraping to extract text content from a given URL.

~ Sumy: Sumy is a library for automatic summarization. It provides tools for extracting the most important sentences from a text document.

Presentation link: Google Slides
Demostration Video: Drive Link

Demonstration Pictures of the Project

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