What Are the Two Types of Text Summarization?

The two main types of text summarization in Natural Language Processing (NLP) are extractive and abstractive summarization. Extractive methods identify and pull key sentences directly from the source text, while abstractive methods generate new sentences to paraphrase and condense the main ideas, similar to human summarization. 

In this blog, you will understand what are the two types of text summarization, how each works, their differences, and where they are used in real-world NLP systems.  

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Types of Text Summarization in NLP 

To understand what are the two types of text summarization, let’s break down both approaches clearly. Below, we will explain how extractive and abstractive summarization work and how they differ in practice. 

• Extractive Summarization 
• Abstractive Summarization 

These two approaches differ mainly in how the final summary is created and how much the original wording is changed. 

Also Read: Natural Language Processing in Machine Learning: Complete Guide  

Here is a quick comparison: 

Type	How It Works	Output Style
Extractive	Selects important sentences from original text	Uses original wording
Abstractive	Generates new sentences based on meaning	Rewritten summary

Both approaches aim to shorten long content while preserving key information. 

The core difference lies in whether the system copies text directly or generates new phrasing based on understanding. 

Also Read: Text Summarization in NLP: Key Concepts, Techniques and Implementation 

A Deep Dive into Extractive Summarization 

Extractive summarization is the older and simpler method. This approach acts like a human using a highlighter on a textbook. The machine scans the original document and identifies the most important sentences based on specific keywords. It then pulls those exact sentences out to create the final paragraph. 

The Core Process of Extraction 

The algorithm scores every sentence mathematically. It looks for word frequency and sentence position. Sentences with the highest scores are selected. 

• The system ignores filler words completely. 
• It ranks sentences based on subject relevance. 
• The final output contains original phrases only. 

Also Read: Text Classification in NLP: From Basics to Advanced Techniques  

Example:  

Original text:  

“The company launched a new product. The launch event was held in Mumbai. The product received positive reviews.”  

Extractive summary:  

“The company launched a new product. The product received positive reviews.”  

The system copies original sentences without rewriting them.  

Also Read: Types of Natural Language Processing with Examples  

Exploring Advanced Abstractive Summarization 

Abstractive summarization is the modern approach powered by advanced neural networks. Instead of copying exact phrases, this method actually understands the core context. It reads the whole document and then writes a completely new summary using entirely different vocabulary. This mimics how a human writer would summarize a book. 

The Power of Abstraction 

This method requires massive computing power. It uses deep learning models to generate natural human language. 

• The model rewrites complex ideas simply. 
• It fixes grammatical errors from the source. 
• The output flows much more naturally. 

Also Read: NLP in Deep Learning: Models, Methods, and Applications  

Example:  

Original text:  

“The company launched a new product. The product received positive reviews.”  

Abstractive summary:  

“The new product launch was well received.”  

The model creates a new sentence while preserving meaning.  

Also Read: Which NLP Model Is Best for Sentiment Analysis in 2026?  

Comparing Both Text Summarization Methods 

Choosing the right method depends on your project goals. Extractive tools are fast and reliable. Abstractive tools are highly advanced but can sometimes generate false facts. Understanding these differences helps developers build better software. 

Feature	Extractive Method	Abstractive Method
Output Type	Copied original text	Brand new sentences
Complexity	Very low	Very high
Hardware Needs	Basic processors	Advanced GPU hardware

Also Read: NLP Models in Machine Learning and Deep Learning  

Conclusion 

Artificial intelligence changes how we read daily. If a colleague ever asks you what are the two types of text summarization, you can now explain extraction and abstraction confidently. Extractive methods highlight existing facts, while abstractive methods create fresh overviews. Knowing how both function allows you to choose the perfect natural language processing tool for your next big project. 

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