Text Summarization in NLP: Key Concepts, Techniques and Implementation

Text summarization in NLP refers to the process of condensing a long piece of text into a shorter, meaningful version. For instance, news aggregators use summarization to quickly provide readers with key updates from lengthy articles. 

One challenge you might face is handling large volumes of content that need quick analysis. 

How text summarization in NLP helps you create quick, meaningful summaries and streamline information processing.

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What is Text Summarization in NLP? Key Concepts and Types

Let’s say you're reading through a 2000-word article about the latest tech trends. You want the key takeaways, but skimming through it all feels like a chore. Here’s where text summarization in NLP steps in. It helps condense long content into bite-sized, meaningful summaries. 

Think about how news apps send you daily briefs, saving you time while giving you the essentials. Whether it’s for news aggregation, research, or document processing, summarization keeps things quick, clean, and to the point.

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But how does text summarization actually work? Here are the key ideas you need to know:

• Text Preprocessing: Before any summarization happens, the text is cleaned up. This includes removing unnecessary words (like "the" and "a"), and breaking it down into sentences or words. 

  For example, imagine you’re summarizing a long product review. Preprocessing would help focus on the main points, removing filler content.

• Tokenization: This is where text gets split into smaller pieces, words or sentences. It’s like chopping up a long paragraph into smaller, digestible bits. 

  If you had an article about cars, tokenization would break it into individual car models, features, or specs, making it easier to pull out the relevant bits.

• Vectorization: This turns words into numbers. Why? Because computers understand numbers, not words. So, instead of a word like “efficient,” the model sees it as a number (or vector). 

  For example, when summarizing news articles, the algorithm converts words into vectors to help it determine which ones matter most.

• TF-IDF (Term Frequency-Inverse Document Frequency): This is a way of finding words that stand out in a document compared to other documents. 

  So, if you were summarizing a tech blog, TF-IDF would highlight words like “AI” or “machine learning” that are specific to that article.

• Word Embeddings: Think of it like teaching the model the meaning of words. Word embeddings like Word2Vec or GloVe map words that are similar (like “car” and “vehicle”) to nearby points in a space. 

  This helps in understanding context. For example, a summarizer for a health blog would recognize that “medication” and “drugs” often mean similar things.

Also Read: Top 25 NLP Libraries for Python for Effective Text Analysis

Types of Text Summarization in NLP

When it comes to summarizing text, there are two main approaches: extractive summarization and abstractive summarization. Each has its own way of cutting through the clutter to get to the essence of a document.

1. Extractive Summarization

When you're swamped with long documents, the last thing you want is to sift through every paragraph just to get the key points. This is where extractive summarization steps in.

The process works by selecting sentences directly from the text that best represent the main ideas. Think of it like a highlighter on your screen: no changes to the wording, just pulling out what’s relevant. 

Here’s how it works:

• Frequency-based Methods: The algorithm looks for words or phrases that pop up a lot and assumes they’re important.
• Graph-based Methods (e.g., TextRank): This method sees the document as a network of sentences. It connects similar sentences and picks out the most “important” ones based on their connections.

While it’s fast, the summaries can sometimes feel a bit choppy, like piecing together random thoughts. And, if you’ve got a lot of repetitive content, it might pick it up too. But when you need something quick and to the point, this method does the job.

Also Read: What is Text Mining in Data Mining? Steps, Techniques & Real-world Applications

2. Abstractive Summarization

When you’re reading through a long article and need a quick summary that actually captures the essence, abstractive summarization can be a lifesaver. Unlike extractive summarization, it doesn’t just pull sentences from the original text. Instead, it rewrites the content in a shorter, more concise form while still keeping the meaning intact.

For example, after reading a lengthy research paper, an abstractive model would create a few lines that sum up the key findings, without copying exact sentences. 

It’s like having a conversation where you explain the main points to someone, but in fewer words.

Here’s how it works:

• Seq2Seq Models: The model reads the input text and then generates a summary by first encoding the content and then decoding it into a shortened version.
• Attention Mechanisms: These help the model focus on the most relevant parts of the text, making sure it doesn’t miss key information while summarizing.

While it sounds neat, it’s a little more complex. The model might occasionally mess up or skip crucial details, and it requires a lot of computing power. But when done right, it can create summaries that flow naturally and read like a human wrote them.

Also Read: 10 Best Data Structures for Machine Learning Model Optimization in 2025

3. Hybrid Summarization

Hybrid summarization combines the best of both extractive and abstractive methods. It starts by extracting key sentences from the original text and then refines those sentences by rephrasing or generating new content. Think of it like taking the most important quotes from an article and then rewording them to make the summary flow more naturally.

This approach works well when you want a concise summary without losing important details. The extracted content helps maintain the core message, while the abstractive step smooths it out and makes the final summary feel more readable.

Here’s how it works:

• Extract and Rewrite: First, the algorithm extracts key sentences, then uses an abstractive model to rephrase or combine them.
• Model-based Approaches: Some models, like BART, handle both extraction and generation in one go, offering a more streamlined process.

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While hybrid summarization tends to produce more coherent results than extractive methods, it’s more computationally demanding. 

Next, let’s move on to how you can actually implement these techniques to create your own summaries.

Implementation of Text Summarization Techniques

Let’s say you’re a developer working with a massive codebase, and you need to quickly grasp the main functions of a specific module without reading through thousands of lines of code. Manually extracting relevant pieces takes too long. 

By implementing extractive or abstractive summarization, you could automate the process of pulling out key code comments or generating a concise summary of what the module does. 

Let’s start with extractive summarization techniques.

Implementing Extractive Summarization Techniques

Let’s say you are a product manager at an e-commerce company, and you need to quickly understand what customers are saying about a new product. You have hundreds of customer reviews, but reading through all of them is a huge time sink. 

So, you decide to automate the process of extracting key insights from these reviews using extractive summarization techniques. Let’s implement these techniques to achieve this:

1. Traditional Method - TF-IDF

TF-IDF (Term Frequency-Inverse Document Frequency) is used to extract the most relevant sentences from a set of reviews. It helps us identify terms that are important to a specific document, as opposed to common words that appear across all documents.

Implementation:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

# Sample customer reviews
reviews = [    "This product is great! I love how easy it is to use.",    "The product is okay, but it broke after a week of use.",    "Excellent value for the price. I would definitely recommend it to others.",    "The product is okay, but the design could be improved. Not durable enough.",    "This product was a waste of money. Terrible quality."]

# TF-IDF Vectorizer
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(reviews)

# Calculate cosine similarity to find the most relevant sentences
cosine_similarities = cosine_similarity(X, X)

# Extract the most similar sentences to the first one
similarity_scores = cosine_similarities[0]  # Compare with the first review
most_similar_idx = np.argsort(similarity_scores)[::-1][1:3]  # Top 2 most similar reviews (excluding itself)

# Display the relevant sentences
print("Most relevant sentences:")
for idx in most_similar_idx:
    print(f"- {reviews[idx]}")

Output:

Most relevant sentences:
- The product is okay, but it broke after a week of use.
- Excellent value for the price. I would definitely recommend it to others.

Explanation:

• TF-IDF Vectorizer: We convert the reviews into numerical form, where the significance of words in each review is calculated using the TfidfVectorizer from scikit-learn.
• Cosine Similarity: We calculate cosine similarity between the reviews. This helps determine how similar the reviews are to each other based on their content.
• Extracting Similar Sentences: After calculating the cosine similarity, we find the two reviews most similar to the first review based on their scores. The result highlights the reviews that mention similar ideas or themes.
• Result: The output shows the most relevant sentences from the reviews. These sentences are selected because they share key themes (e.g., product quality and customer satisfaction).

Also Read: Math for Data Science: A Beginner’s Guide to Important Concepts

2. Graph-Based Method - TextRank

TextRank is a graph-based algorithm used for extractive summarization. It works by constructing a graph where each sentence in the document is represented as a node, and edges represent the similarity between sentences. 

The algorithm ranks sentences based on their connections and importance in the text. The sentences with the highest importance scores are selected as the summary.

Implementation: 

from summa import summarizer

# Sample customer reviews
reviews_text = """
    This product is great! I love how easy it is to use.
    The product is okay, but it broke after a week of use.
    Excellent value for the price. I would definitely recommend it to others.
    The product is okay, but the design could be improved. Not durable enough.
    This product was a waste of money. Terrible quality.
"""

# Use TextRank to extract summary
summary = summarizer.summarize(reviews_text)

print("TextRank Summary:")
print(summary)

Output: 

TextRank Summary:
The product is okay, but it broke after a week of use.
This product was a waste of money. Terrible quality.

Explanation:

• TextRank Algorithm: We use the summa library, which implements the TextRank algorithm. The algorithm builds a graph from the input text and ranks sentences based on their centrality in the document.
• Sentence Selection: The sentences selected are those with the highest centrality, i.e., the most important sentences that summarize the key ideas of the document.
• Result: The sentences selected in the summary highlight key aspects of customer feedback: product quality issues and customer dissatisfaction.

Also Read: Top 10 Data Science Algorithms Every Data Scientist Should Know

3. Modern Extractive Model - BERT

BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art model in NLP that can be used for extractive summarization. Unlike traditional methods like TF-IDF or TextRank, BERT understands the context of words in a sentence. 

This allows it to select the most relevant sentences based on their meaning rather than just their frequency or relationships to other sentences.  

Implementation: 

from transformers import pipeline

# Load pre-trained BERT model for extractive summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Sample customer reviews
reviews_for_bert = """
    This product is great! I love how easy it is to use.
    The product is okay, but it broke after a week of use.
    Excellent value for the price. I would definitely recommend it to others.
    The product is okay, but the design could be improved. Not durable enough.
    This product was a waste of money. Terrible quality.
"""

# Use BERT to extract summary
summary_bert = summarizer(reviews_for_bert, max_length=150, min_length=30, do_sample=False)

print("BERT Summary:")
print(summary_bert[0]['summary_text'])

Output: 

BERT Summary:
This product is great! I love how easy it is to use. Excellent value for the price. I would definitely recommend it to others. The product is okay, but it broke after a week of use.

Explanation:

• BERT Model: We use the Hugging Face transformers library to load a pre-trained BERT model (facebook/bart-large-cnn). 
• Summarization: The summarizer pipeline processes the input text (customer reviews), and the model generates a concise summary. 
• Result: The output shows a summary that captures positive customer feedback, such as ease of use and value for money, while also mentioning issues with product durability.

Also Read: Advanced AI Technology and Algorithms Driving DeepSeek: NLP, Machine Learning, and More

Now that you've seen how extractive summarization techniques work, let's move on to the next set of techniques. 

Implementing Abstractive Summarization Techniques

If you're dealing with complex content, like research papers, long-form articles, or dense reports extractive methods can feel disjointed and lack cohesion. Abstractive summarization lets you create concise, meaningful summaries that flow naturally while retaining the core ideas. Let’s implement these techniques to achieve this:

1. Sequence-to-Sequence Models (Seq2Seq)

Seq2Seq (Sequence-to-Sequence) models use an encoder-decoder architecture to transform an input sequence (such as a paragraph or document) into an output sequence (a shorter summary). The encoder processes the input text and encodes it into a fixed-length vector, which the decoder then uses to generate a corresponding summary.

Implementation: 

from transformers import pipeline

# Load pre-trained T5 model for abstractive summarization
summarizer = pipeline("summarization", model="t5-small")

# Sample text
input_text = """
    The moon is Earth's only natural satellite and the fifth largest moon in the Solar System. 
    It is the brightest object in the night sky and has a profound impact on Earth's tides.
    Humans have studied the moon for centuries, and it was the destination of the first human landing on another celestial body in 1969.
"""

# Use T5 model for abstractive summarization
summary = summarizer(input_text, max_length=100, min_length=30, do_sample=False)

print("Seq2Seq Summary:")
print(summary[0]['summary_text'])

Output: 

Seq2Seq Summary:
The moon is Earth's only natural satellite, and it has a profound impact on Earth's tides.

Explanation:

• Encoder-Decoder Architecture: The encoder reads the input text (in this case, information about the moon) and encodes it into a fixed-size vector. 
• Summarization: The T5 model (a Seq2Seq model) uses this architecture to generate a more natural and coherent summary, understanding both the structure and meaning of the input.
• Result: The output is a short, meaningful summary that retains the key ideas from the original text, such as the moon's relationship to Earth and its impact on tides, while eliminating less relevant details.

Also Read: Image Segmentation Techniques [Step By Step Implementation]

2. Transformer Models for Abstractive Summarization: BERT, T5, BART

Transformer models like BERT, T5, and BART are the backbone of modern abstractive summarization. These models understand the context of the entire input text, allowing them to generate summaries that are both fluent and contextually relevant. 

Unlike traditional Seq2Seq models, transformer models use self-attention mechanisms to capture relationships between words, no matter how far apart they are in the text.

Implementation: 

from transformers import pipeline

# Load pre-trained BART model for abstractive summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Sample text
input_text = """
    Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals.
    Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
"""

# Use BART model for abstractive summarization
summary_bart = summarizer(input_text, max_length=100, min_length=30, do_sample=False)

print("BART Summary:")
print(summary_bart[0]['summary_text'])

Output: 

BART Summary:
Artificial intelligence (AI) is intelligence demonstrated by machines, in contrast to the intelligence displayed by humans and animals.

Explanation:

• Transformer Architecture: BART is a transformer-based model that combines the encoder-decoder architecture, using self-attention to understand the full context of the text. 
• Summarization: The model processes the input text, then generates a summary that captures the most essential information while discarding less relevant details.
• Result: The summary produced by BART retains the core message about AI, describing its definition and contrast with natural intelligence.

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Generating summaries is only part of the picture, knowing how to evaluate their quality is just as important. Next, let's look at some evaluation metrics for these techniques. 

 Evaluation Metrics for Summarization Techniques

Suppose you're summarizing news articles automatically for a media company. In that case, you might be happy with the summaries at first, but how do you know if they genuinely reflect the key points of the original articles?

Without a clear standard, it’s easy to miss crucial details or generate irrelevant summaries. Let’s now look at the key evaluation metrics:

1. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

ROUGE is the most widely used metric for evaluating summarization. It compares the overlap between n-grams (unigrams, bigrams, etc.) in the generated summary and the reference (human-written) summary.

It measures recall, precision, and F1-score between the generated summary and the reference.

If your model generates the summary "The car is fast and efficient," and the reference summary is "The car is quick and efficient," ROUGE would give a high score for the overlap between "efficient" and "car."

Key ROUGE Metrics:

• ROUGE-N: Measures overlap of n-grams (e.g., ROUGE-1 for unigrams, ROUGE-2 for bigrams).
• ROUGE-L: Measures the longest common subsequence between the generated summary and the reference.

2. BLEU (Bilingual Evaluation Understudy)

Originally developed for machine translation, BLEU measures how many n-grams in the generated summary match those in a reference summary.

BLEU compares precision for n-grams, but with a penalty for overly short summaries (to avoid “overfitting”).

If the reference summary is "The phone is fast and user-friendly" and the generated summary is "The phone is quick and easy to use," BLEU evaluates how well the generated text matches the reference n-grams like "phone," "fast," "quick."

3. METEOR (Metric for Evaluation of Translation with Explicit ORdering)

METEOR is another metric used to evaluate machine-generated text. It measures both exact word matches and synonymy (i.e., it considers different words with similar meanings as matches).

METEOR uses precision and recall, but adds a penalty for reordering words and considers synonym matches to provide a more flexible evaluation.

If the reference summary says, "The movie was entertaining," and the generated summary is "The film was enjoyable," METEOR would reward the synonym match between "movie" and "film" as well as "entertaining" and "enjoyable."

4. Content Overlap

Content overlap measures how much of the important content from the original document is retained in the summary. This can be done manually or with automated systems that look for specific key terms or ideas.

This metric ensures that the key points from the original content aren’t left out in the summary

For an article about climate change, a good summary would include important terms like "greenhouse gases," "global warming," and "emissions." A low content overlap would mean these important points are missing.

5. Human Evaluation

While automated metrics are useful, human evaluation remains a strong method for assessing summarization quality. Human judges read both the original document and the generated summary, scoring them on aspects such as coherence, relevance, fluency, and informativeness.

A human evaluator might rate a summary of a news article as "good" if it captures the main points of the event without losing crucial details, but would rate it as "poor" if it’s too vague or misses key facts.

Begin by experimenting with different summarization methods on various datasets, fine-tuning your models for better results. 

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To further build on your skills, topics like summarization of long documents, multimodal summarization, and zero-shot summarization with minimal training might be worth exploring. 

Advance Your Machine Learning Skills with upGrad!

Text Summarization in NLP enables you to capture the core ideas of a document, either by extracting key sentences directly or by generating completely new summaries. While these methods are a great starting point, the challenge lies in balancing summary quality with available computational resources.

To improve your summarization efforts, focus on refining your models by experimenting with different datasets and evaluating them through metrics like ROUGE and BLEU. If you’re looking to deepen your understanding of NLP, upGrad offers specialized courses that can help you build on your machine-learning skills.  

In addition to the courses mentioned above, here are some more free courses that can help you enhance your skills:  

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Reference:
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