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.

Hiring signal: Understanding of text summarization internals

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: Phase 5 · 02 (BoW + TF-IDF), Phase 5 · 11 (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.

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You've read the first 2 sections. The rest of this lesson covers The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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