Project aims to develop a text summarization tool using natural language processing (NLP) techniques integrated into a Flask web application. This tool allows users to input lengthy text documents and receive summarized versions, facilitating quicker comprehension and decision-making.
Our project directory is structured as follows:
CORE: Contains the main functionalities of the application.
summarize_text.py: Implements text summarization using spaCy.
reading_time.py: Calculates the estimated reading time for a given text.
Template: Holds the HTML template for the web interface.
index.html: Provides the user interface for text input and display of summaries.
app.py: The main Flask application file, handling routing and interactions between the front end and back end.
Text Summarization Algorithm: We utilize spaCy, a powerful NLP library, to perform text summarization. The algorithm involves tokenization, sentence scoring based on word frequency and sentence position, and extraction of the most relevant sentences using heapq's nlargest function.
Reading Time Estimation: We calculate the estimated reading time of the original text and the summary using a custom function get_reading_time() in reading_time.py. This function considers the average reading speed of users to provide a realistic estimate.
Flask Integration: Flask serves as the backbone of our web application. The app.py file defines routes for handling user requests, including text input, processing, and displaying the results. It communicates with the summarization and reading time modules to generate the summary and relevant statistics.
To use our text summarization tool:
Clone the repository to your local machine.
Install the required dependencies.
Run the Flask application by executing python app.py.
Access the application through a web browser.
Input the desired text into the provided text area.
Click the "Summarize" button to generate a summary.
The original text, summary, total word count, and reading time will be displayed.
In conclusion, text summarization project demonstrates the seamless integration of NLP techniques with web development using Flask. While the current version provides basic summarization functionality, future enhancements could include:
Enhanced summarization algorithms for improved accuracy.
User authentication and document management features.
Integration with external APIs for fetching and summarizing web content.
Support for different languages and file formats.
