textteaser is an automatic text summarization algorithm implemented in Python. It extracts the most important sentences from an article to generate concise summaries that retain the core meaning of the original text. The algorithm uses features such as sentence length, keyword frequency, and position within the document to determine which sentences are most relevant. By combining these features with a simple scoring mechanism, it produces summaries that are both readable and informative. Originally inspired by research and earlier implementations, textteaser provides a lightweight solution for summarization without requiring heavy machine learning models. It is particularly useful for developers, researchers, or content platforms seeking a simple, rule-based approach to article summarization.
Features
- Automatic text summarization through sentence extraction
- Uses features like length, keyword frequency, and sentence position
- Produces concise summaries while preserving key information
- Lightweight Python implementation for easy integration
- Does not require large datasets or training processes
- Useful for news articles, documents, and other textual content