This project is an AI-powered text summarization tool created by Zain Qamar as Task 3 for the Certura Internship.
This web application provides a simple yet powerful way to condense long articles, documents, or paragraphs into concise, easy-to-read summaries. The tool is designed to extract the most important sentences, preserving the core meaning of the original text.
Users can either paste text directly into a text area or upload a .txt file. The application then displays the summary along with statistics on the reduction in length and provides an option to download the summary as a text file.
The application's core logic is built with Python, using the Flask web framework to handle server requests. For the Natural Language Processing (NLP) tasks, it leverages the powerful spaCy library.
The summarization process is as follows:
- The user provides text via a textarea or file upload.
- The Flask backend receives the text.
- spaCy processes the text to understand its linguistic structure (tokenization, sentence segmentation, part-of-speech tagging).
- The algorithm calculates the frequency of important words (excluding common "stop words").
- Each sentence is scored based on the frequency of the words it contains.
- The highest-scoring sentences are selected to form the final summary.
- The summary and statistics are sent back to the frontend, which is built with HTML, CSS, and modern JavaScript to create a dynamic and responsive user experience.