Text Summarizer: Quick Python CLI for Extracting Key Sentences from Documents

When working with long documents—meeting notes, research papers, technical documentation—you often need a quick way to grasp the main points without reading everything. The Text Summarizer is a lightweight Python CLI tool that extracts key sentences based on frequency analysis and sentence boundary detection.

What Problem Does It Solve?

Extracting summaries from text documents traditionally requires:

• Manual reading (time-consuming)
• Complex NLP libraries like spaCy or NLTK (heavy dependencies)
• Paid summarization APIs

Text Summarizer provides a simple, dependency-free alternative using pure Python to identify and extract the most important sentences for quick overviews.

Installation

# Download and use directly
git clone https://github.com/Poolion/text-summarizer.git
cd text-summarizer
python text-summarizer.py document.txt --words 100 --top-sentences 3

Add to your PATH:

cp text-summarizer.py /usr/local/bin/

Usage Examples

Basic Document Summarization

python text-summarizer.py my-article.md --words 150 --top-sentences 5
# Extracts top 5 sentences from an article, limited to 150 words

Handle long documents:

python text-summarizer.py research-paper.pdf.txt --words 200 --top-sentences 8

Pipe Input from stdin

echo "Meeting notes with multiple paragraphs..." | python text-summarizer.py --top-sentences 2

Process files in a loop:

for file in reports/*.txt; do
    python text-summarizer.py "$file" --words 80 --top-sentences 1
done

How It Works

Sentence Boundary Detection

The tool correctly handles common abbreviations that often confuse simple period-based splitters. For example:

• "Dr. Smith said..." (doesn't split at the period after Dr)
• "Mr. Johnson arrived" (same treatment)
• "Jr." in "John Sr. and John Jr."

The regex pattern filters these intelligently while still splitting on actual sentence-ending punctuation like . followed by space or newline, !, and ?.

Frequency-Based Selection

Currently, the tool extracts sentences from the beginning of the document. For production use, you could extend it with:

• TF-IDF scoring to weight sentences by unique word importance
• Statistical algorithms like TextRank (Graph-based ranking)
• Extractive methods using sentence similarity scores

Why Use This Tool?

• No dependencies: Pure Python standard library only
• Fast processing: Works on large documents in seconds
• Configurable limits: Adjust word count and sentence count as needed
• Pipeline-friendly: Stream input from stdin or pipe from other commands
• Lightweight: Under 50 lines of code, easy to extend

When to Use It

Ideal for:

• Quick document previews before deep reading
• Meeting notes summarization (grab key points)
• Documentation triage (find relevant sections)
• Email/newsletter content extraction
• Report quick-scanning workflows

Limitations

This is an extractive summarizer. Unlike abstractive models that generate new sentences, this tool selects existing ones. For more sophisticated summarization you'd need neural networks like BART or Pegasus—but those require GPU and much larger models.

For lightweight document preview needs, Text Summarizer provides a good balance of simplicity and effectiveness.

Conclusion

Text Summarizer is a simple but practical CLI tool for extracting key content from large documents without heavy dependencies. It's perfect for quick document triage and understanding long articles in seconds.

Repo: https://github.com/Poolion/text-summarizer

If you find this useful, you can support development: https://www.buymeacoffee.com/poolion