Text Summarization in Python — ELI5

You have a 20-page report to read before a meeting in ten minutes. You wish someone could hand you a three-sentence version with just the important parts. Text summarization does exactly that — but with a computer instead of a helpful coworker.

There are two ways a computer can summarize.

The first way is like using a highlighter. The computer reads through the text, picks out the most important sentences, and strings them together. It does not write anything new — it just selects the best existing sentences. This is called extractive summarization.

The second way is like asking a friend to explain the article in their own words. The computer reads everything, understands the key ideas, and writes brand-new sentences that capture the meaning. This is called abstractive summarization. It is harder but often produces more natural-sounding summaries.

How does the computer know which sentences are important? For the highlighter method, it looks at clues: sentences at the beginning of paragraphs are often important, sentences with keywords that repeat throughout the document matter more, and sentences connected to many other sentences tend to carry the main ideas.

For the “explain it in your own words” method, modern systems use large language models that have read millions of articles and their summaries. They learned patterns about how to compress information, similar to how you learned to write book reports in school.

A common mix-up is thinking computer summaries are always accurate. They are not. Sometimes the computer picks unimportant sentences, misses key details, or — with the second method — invents facts that were not in the original text. Always double-check summaries for important decisions.

The one thing to remember: Text summarization shrinks long text into short versions, either by picking the best existing sentences or by writing new ones — saving you time when you need the gist without the full read.