Phase 1: Text Representation · ~75 minutes · Python

Text Summarization

Extractive systems tell you what the document said. Abstractive systems tell you what the author meant. Different tasks, different pitfalls.

What you will learn

Implement text summarization from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build
Languages: Python
Prerequisites: Bag of Words, TF-IDF, and Text Representation, Machine Translation
Time: ~75 minutes

The Problem

A 2,000-word news article lands in your feed. You need 120 words that capture it. You can either pick the three most important sentences from the article (extractive) or rewrite the content in your own words (abstractive). Both are called summarization. They are completely different problems.

Extractive summarization is a ranking problem. Score every sentence, return the top-k. The output is always grammatical because it is lifted verbatim. The risk is missing content that is distributed across the article.

Abstractive summarization is a generation problem. A transformer produces new text conditioned on the input. The output is fluent and compressive but may hallucinate facts that were not in the source. The risk is confident fabrication.

This lesson builds both, with the failure mode each one owns.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

Create a free account — the opening phases of 29 of 35 courses are free, no credit card.