This project consists of a Python script that performs web scraping of an online article and then generates a summarization of the most important information contained in that article. The summarization is based on word frequency and sentence relevance.
Before running the script, make sure you have the following libraries installed:
- NLTK (Natural Language Toolkit): A natural language processing library for Python.
- BeautifulSoup: A library for parsing HTML documents.
- Python 3.x: Python version 3.x is required to run the code.
You may also need to download linguistic resources using the nltk.download() command.
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Clone the repository or download the script.
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Run the Python script, ensuring that all necessary libraries are installed.
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The script will perform web scraping on a sample article (you can replace the URL in the code to use a different article) and generate a summarization of the most important sentences.
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The most important sentences will be printed to the standard output.
You can customize this project to use different article URLs or adjust summarization parameters, such as the number of most important sentences to display.
This project was created by wesleyclzns.
This project was contributed by Davidgts
This project is licensed under the MIT License - see the LICENSE for details.