This project is a text summarization tool that automatically generates concise and coherent summaries of long text documents. Text summarization is a vital task in natural language processing and can be used for a variety of applications, including content extraction, information retrieval, and more.
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Extractive Summarization: This tool uses an extractive summarization approach, which selects and condenses the most important sentences from the input text to create a summary.
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Stopwords and Punctuation Handling: The tool removes common stopwords and punctuation from the text to focus on meaningful content during the summarization process.
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Word Frequency-Based Scoring: It calculates word frequencies in the input text and assigns scores to sentences based on the frequency of important words.
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Customizable Summary Length: You can control the length of the generated summary by specifying the desired percentage of the original text's length.
- Python 3.x
- Spacy
- Other required dependencies (if any)
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Clone this repository to your local machine.
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Install the necessary dependencies, if not already installed, using
pip:pip install spacy
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Download the spaCy English model:
python -m spacy download en_core_web_sm
To summarize a text document, you can enter the text in the textarea and press the "Summarize" button.
You can customize the summarization process by adjusting parameters such as summary length, stopwords, or punctuation handling in the summarizer function.
Contributions to this project are welcome. If you have ideas for improvements or want to fix issues, please feel free to create pull requests or open new issues.
This project is licensed under the MIT License - see the LICENSE file for details.
- spaCy: Used for natural language processing and tokenization.
- NLTK: Used for stop words and other NLP tasks.
If you have any questions or suggestions, you can raise them.