text-summarizer

Generate extractive summaries from long text documents using TextRank, LSA, or frequency-based methods.

86|18|Updated Dec 14, 2025

Search Tags:#document processing #summarization #nlp #text analysis #information extraction #content condensation

Ready to run

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill text-summarizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: text-summarizer
Source: https://github.com/dkyazzentwatwa/chatgpt-skills/tree/main/text-summarizer
Command: npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill text-summarizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires nltk, numpy, scikit-learn, and includes scripts (resource) components.

What problem does it solve?

This Skill addresses the challenge of information overload by providing concise summaries of lengthy text documents, helping users quickly grasp the main points without reading the entire content.

Core Features & Use Cases

Extractive Summarization: Selects the most important sentences from the original text.
Length Control: Summaries can be adjusted by ratio, sentence count, or word count.
Batch Processing: Efficiently summarize multiple documents at once.
Use Case: Quickly generate executive summaries for reports, condense research papers, or extract key takeaways from meeting transcripts.

Quick Start

Use the text summarizer skill to generate a summary of the provided article text, keeping 20% of the original length.

Frequently Asked Questions about text-summarizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I summarize long text documents using extractive methods?▼

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.

Can I control the summary length by ratio, sentence count, or word count?▼

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.

Does this text summarization tool support batch processing of multiple documents?▼

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.

What is the best way to extract key takeaways from meeting transcripts or research papers?▼

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.

Do I need nltk and scikit-learn to perform text analysis and content condensation?▼

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.