What Are The 5 Steps in Summarizing a Text In NLP?

The 5 key steps in extractive text summarization in NLP are: text preprocessing (cleaning, tokenization, stop-word removal), sentence tokenization, sentence scoring (e.g., using TF-IDF or graph-based methods), selecting the top-ranked sentences, and generating the final summary. 

In this blog, you will learn what are the 5 steps in summarizing a text, understand each step clearly, and see how NLP systems generate accurate summaries. 

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Five Key Steps in Summarizing a Text in NLP 

To understand what are the 5 steps in summarizing a text, you need to look at the structured pipeline NLP systems follow. Each step plays a specific role in transforming long content into a clear and concise summary. 

• Text Preprocessing 
• Sentence Segmentation 
• Feature Extraction or Representation 
• Scoring and Ranking 
• Summary Generation and Evaluation 

These five steps form the backbone of both extractive and abstractive summarization models. While advanced neural systems automate many parts internally, the core logic still follows this pipeline. 

Here is a quick overview: 

Step	Purpose
Preprocessing	Clean and normalize text
Segmentation	Split text into sentences
Feature Extraction	Convert text into numerical form
Scoring	Rank important sentences
Generation	Create final summary

Each step plays a specific role. Preprocessing prepares the data. Segmentation organizes it. Feature extraction enables computation. Scoring identifies key information. Generation produces the final concise version. 

Now let’s explore each step in detail. 

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

Step 1: Text Preprocessing 

The first step in understanding what are the 5 steps in summarizing a text is preprocessing. This stage prepares raw text for analysis by removing noise and standardizing the content. 

This step includes: 

• Removing stop words 
• Lowercasing text 
• Removing punctuation 
• Tokenization 

Preprocessing ensures the model focuses only on meaningful words and patterns instead of unnecessary symbols or filler terms. 

Example: 

Original text: 
“The product, which was launched in 2023, is highly innovative.” 

After preprocessing, the sentence is cleaned and simplified so the system can analyse important words like “product,” “launched,” and “innovative” more effectively. 

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

Step 2: Sentence Segmentation 

The second step in understanding what are the 5 steps in summarizing a text is sentence segmentation. This process breaks a document into individual sentences so the model can analyze them separately. 

Why is this important? 

• It organizes long text into manageable units. 
• It allows the system to score each sentence independently. 
• It helps identify which sentences carry key information. 

For example, a news article may contain 20 sentences. Segmentation allows the model to evaluate each one and determine which are most relevant for the final summary. 

Also Read: Types of Natural Language Processing with Examples 

Step 3: Feature Extraction or Text Representation 

The third step in understanding what are the 5 steps in summarizing a text is feature extraction, also called text representation. In this stage, the system converts words and sentences into numerical form so algorithms can process them. 

Common methods include: 

• TF-IDF vectors 
• Word embeddings 
• Transformer-based embeddings 

This step allows the model to measure similarity, relevance, and importance. Without converting text into numbers, the system cannot rank or compare sentences effectively. 

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

Step 4: Scoring and Ranking 

The fourth step in understanding what are the 5 steps in summarizing a text is scoring and ranking. At this stage, the system evaluates each sentence and assigns an importance score. 

Scoring may depend on: 

• Keyword frequency 
• Sentence position 
• Similarity to the main topic 
• Relevance to the overall theme 

Higher-scoring sentences are selected for extractive summaries. In neural models, attention mechanisms help determine which parts of the text matter most. This step ensures only the most important information moves forward to the final summary. 

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

Step 5: Summary Generation and Evaluation 

The final step in understanding what are the 5 steps in summarizing a text is summary generation and evaluation. After ranking the most important information, the system produces the final condensed version. 

There are two main approaches: 

• Extractive: Select top-ranked sentences directly from the original text. 
• Abstractive: Generate new sentences that capture the main idea. 

Once generated, the summary is evaluated using metrics such as ROUGE to measure accuracy and relevance. This step ensures the output is clear, concise, and meaningful. 

Also Read: NLP Models in Machine Learning and Deep Learning 

Extractive vs Abstractive Summarization 

To better understand what are the 5 steps in summarizing a text, it is important to know the two main approaches used in NLP systems. Both follow the same five-step process, but they differ in how the final summary is produced. 

Type	How It Works	Example
Extractive	Selects important sentences directly from the original text	News highlights
Abstractive	Generates new sentences that capture the main idea	AI-generated summaries

The core steps remain the same, but the generation method makes the difference. 

Also Read: 15+ Top Natural Language Processing Techniques 

Conclusion 

Understanding what are the 5 steps in summarizing a text helps you see how NLP systems turn long content into concise summaries. From preprocessing to evaluation, each step plays a clear role in identifying and generating key information. Whether extractive or abstractive, this structured process ensures summaries remain accurate, relevant, and meaningful. 

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