Welcome to the Advanced Extractive Text Summarization Model! This project uses Natural Language Processing (NLP) techniques to automatically distill essential points from lengthy content, making it an invaluable tool for handling reports, research papers, news articles, and more.
This model leverages NLP to:
- Extract key sentences from a body of text.
- Score sentences based on their importance using features like TF-IDF, sentence length, position, and presence of named entities.
- Cluster related sentences via K-means to highlight critical points from various thematic groups.
In today’s information-dense world, quickly understanding critical points from long documents is essential. This model saves time and boosts productivity by providing concise summaries while preserving core insights.
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Preprocessing
- Cleans and prepares text data for effective summarization.
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Scoring & Ranking
- Scores sentences based on TF-IDF, sentence structure, and key entities.
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Clustering & Key Point Extraction
- Uses K-means clustering to group sentences by topic and select key sentences for each group.
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Summary Generation
- Combines top-ranked sentences from each cluster to create a coherent, impactful summary.
- Data Preprocessing: Initial cleaning (e.g., removing stop words, punctuation).
- Sentence Scoring: Uses TF-IDF, sentence structure, and named entity recognition to evaluate sentence importance.
- K-means Clustering: Groups related sentences to capture diverse perspectives within the text.
- Summarization: Extracts top sentences across clusters to create a balanced summary.
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Clone the Repository:
git clone https://github.com/one-alive/extractive_text_summarization.git cd extractive_text_summarization -
Install Dependencies:
pip install -r requirements.txt
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Run the Model on a Sample Text:
python summarize.py
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Adjust Parameters: You can tune parameters such as the number of clusters, sentence selection criteria, and summary length for better results based on the text type.
- Parameter Tuning: Experiment with different clustering techniques and scoring weights.
- Expand Dataset Compatibility: Optimize for specific types of documents like research papers or news articles.
- Add Fine-Tuning: Integrate more NLP models to improve summarization accuracy.
Contributions are welcome! If you have ideas or suggestions, please create a pull request or open an issue.
If you have questions or want to explore collaboration opportunities, feel free to reach out!