text-summarizer

Extractive summarization selects the most important sentences from original text using algorithms like TextRank, LSA, and frequency-based methods. This approach distills key insights without generating new text, preserving the original meaning while reducing length.

Summary length is adjustable by specifying a ratio, exact sentence count, or target word count. This flexibility allows precise control over information condensation, whether you need a brief overview or a detailed executive summary.

Batch processing is supported, enabling efficient summarization of multiple documents at once. This feature is designed for high-throughput information extraction, allowing you to condense large volumes of text simultaneously.

To extract key takeaways, apply frequency-based or TextRank summarization algorithms to the transcript or paper. These methods identify and rank the most significant sentences, providing a concise overview of the core content.

Yes, nltk, numpy, and scikit-learn are required dependencies for this text analysis and content condensation. These libraries provide the foundational NLP and mathematical operations needed for sentence ranking and information extraction.