Text Summarization in NLP: Techniques, Algorithms, and Real-World Applications

With over 500 million tweets posted daily, alongside a surge in AI-generated content and massive data streams from social media, information overload is unavoidable. Text summarization in NLP helps filter, condense, and extract key insights. Users can efficiently process vast amounts of content without being overwhelmed by excessive information.

This guide covers essential techniques, algorithms, and real-world applications of text summarization in NLP. It is indispensable for NLP practitioners and data professionals trying to effectively process and summarize vast amounts of information.

What is Text Summarization in NLP? Key Concepts

Text summarization in NLP involves using algorithms to automatically condense large bodies of text into concise summaries while retaining the most critical information.

It condenses large texts into concise summaries using two approaches, extractive and abstractive summarization. 

Extractive summarization selects key sentences from the original text (e.g., Google News highlights). 

Abstractive summarization generates new, paraphrased content using transformer models (e.g., ChatGPT and OpenAI APIs for summarizing legal and financial documents).

Let’s explore a few real world applications of text summarization in NLP:

• Media: Apps like Inshorts and Pocket use summarization to deliver news briefs, keeping readers updated without overwhelming them with information.
• Publishing & Learning: Platforms like Blinkist summarize non-fiction books into quick, insightful reads, while YouTube's AI - generated summaries help users grasp video content at a glance.
• Customer Service: Companies like Zendesk integrate summarization tools to condense customer interactions, allowing agents to respond faster and more efficiently.

In today’s fast-paced, data-driven world, these summarization techniques are transforming how information is processed and consumed across various domains.

Also Read: 30 Natural Language Processing Projects in 2025 [With Source Code]

Now that you understand the key concepts of text summarization, it’s important to consider how different approaches—extractive and abstractive—impact the summarization process.

How Does Text Summarization Work? Methods and Mechanisms

Text summarization relies on a combination of sophisticated algorithms, machine learning models, and data preprocessing techniques to generate concise summaries. 

The process begins with preparing the raw text for analysis and then applying either extractive or abstractive methods, depending on the complexity and desired output.

1. Preprocessing Steps

Before summarization begins, preprocessing cleans and structures the data for better accuracy and efficiency:

• Tokenization: Splitting text into sentences or words.
• Removing Stop Words: Filtering out common words (e.g., "the," "is") that don’t add value.
• Stemming and Lemmatization: Reducing words to their base or root form.
• POS Tagging: Identifying parts of speech to help determine sentence importance.
• Vectorization: Converting text into numerical representations (e.g., TF-IDF vectors) for algorithmic processing.

Also Read: Steps in Data Preprocessing: What You Need to Know?

2. Extractive Methods

Extractive summarization selects the most important sentences directly from the text, often using ranking-based methods like TextRank. These algorithms build a graph where sentences are nodes, and edges represent similarities based on shared words or phrases. 

Using an approach similar to Google's PageRank, TextRank assigns importance scores to sentences, prioritizing those that are most connected to others. 

This makes extractive summarization both fast and effective, ideal for applications like news aggregation and document indexing.

• Frequency-Based Methods: Algorithms like TF-IDF measure the importance of a word based on its frequency across documents. High-frequency terms typically indicate key points.
• Graph-Based Methods: Algorithms like TextRank and PageRank represent sentences as nodes in a graph, where edges represent similarity scores. Sentences with higher connections are considered more important.

Example: Google News uses graph-based extractive summarization to deliver quick, relevant news snippets from multiple sources.

3. Abstractive Methods

Abstractive summarization goes beyond copying sentences—it generates new, concise phrases by understanding the content’s context. While more advanced, it comes with challenges like hallucination, where models generate inaccurate information. 

To improve factual accuracy, techniques like fine-tuning on domain-specific datasets or reinforcement learning with human feedback are used. 

• Seq2Seq Models: Early models like LSTM-based encoder-decoder frameworks improved upon n-gram models by capturing context more effectively. However, they still struggled with very long dependencies due to limitations in retaining distant contextual information. Transformers later surpassed LSTMs, offering better long-range context handling in text summarization.
• Transformers: Modern transformer-based models like BERT, T5, and GPT-4 have revolutionized abstractive summarization. These models leverage self-attention mechanisms to process entire texts simultaneously, improving coherence and contextual understanding.

Example: OpenAI’s GPT-powered tools and Google’s Gemini are widely used for summarizing lengthy legal, financial, and technical documents.

Here’s how you can choose the right approach:

• Extractive summarization is ideal for structured, factual content like news articles and reports.
• Abstractive summarization is preferred when paraphrasing or simplifying complex narratives, such as summarizing research papers or legal documents.

Also Read: Generative AI in Practice: Real-World Use Cases and Success Stories

Algorithms for Text Summarization: Key Approaches

Algorithms are the backbone of text summarization, determining how effectively and accurately information is condensed. They play a crucial role in both extractive and abstractive summarization.

• Ranking and Similarity-Based Approaches: Extractive methods often rely on ranking sentences based on similarity and importance. Algorithms like PageRank and TextRank assess relationships between sentences to identify key points.
• Neural-Based Advancements: Abstractive summarization has seen significant improvements with the introduction of transformer architectures and pre-trained models like BERT and T5, enabling machines to generate more human-like summaries.

Here are the key algorithms:

Also Read: Top 5 Machine Learning Models Explained For Beginners

Evaluating Text Summarization Techniques in NLP

Evaluating the effectiveness of text summarization techniques in NLP is critical for ensuring high-quality outputs. Evaluation methods are broadly categorized into intrinsic and extrinsic approaches.

Here’s a table with their descriptions and examples:

You can use domain-specific vs. domain-independent methods:

• Domain-Specific: In specialized fields like medicine or law, models are fine-tuned on domain-specific datasets to improve summarization accuracy and relevance.
• Domain-Independent: General-purpose models are evaluated across various datasets, ensuring they perform well in diverse contexts.

BLEU Score Calculation (with Code Example)

The BLEU (Bilingual Evaluation Understudy) score is a widely used metric for evaluating machine-generated text, particularly in abstractive summarization and machine translation. BLEU works by comparing n-grams (sequences of words) in the generated summary to those in a reference summary written by a human.

The score ranges from 0 to 1, where 1 indicates a perfect match with the reference text. However, BLEU has limitations, as it primarily measures n-gram overlap and does not fully capture fluency, coherence, or factual correctness—which makes it less ideal for summarization tasks.

Here are its key concepts:

• N-gram Overlap: BLEU measures how many n-grams (unigrams, bigrams, trigrams, etc.) from the generated text match the reference text.
• Precision-Based: It focuses on how many of the generated words are correct, rather than whether all necessary content is covered.
• Brevity and BLEU: BLEU primarily measures n-gram overlap between generated and reference summaries, often favoring shorter outputs. However, it does not effectively assess summary quality, coherence, or informativeness. Alternative metrics like ROUGE, which compare recall and precision of key phrases, are better suited for evaluating summarization tasks.

Code Example (Python):

from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

# Reference summary (human-generated)
reference = [['this', 'is', 'an', 'example', 'summary']]

# Candidate summary (machine-generated)
candidate = ['this', 'is', 'example', 'summary']

# Apply smoothing to prevent zero scores for short texts
smooth = SmoothingFunction().method1

# Calculate BLEU score
score = sentence_bleu(reference, candidate, smoothing_function=smooth)
print(f'BLEU Score: {score:.2f}')

Explanation:

• Reference: A human-generated summary provided as a list of tokenized words within another list (to support multiple references).
• Candidate: The machine-generated summary being evaluated.
• BLEU Calculation: The sentence_bleu function measures n-gram overlap between the reference and candidate. Since BLEU over-penalizes short texts, smoothing is applied to produce a more realistic score.

Expected Output:

BLEU Score: 0.19

Interpretation: A BLEU score of 0.19 suggests low to moderate similarity between the generated and reference summaries. The missing word "an" reduces bigram and trigram overlap, which significantly affects BLEU's precision-based evaluation.

• In real-world applications, BLEU struggles to evaluate abstractive summarization because it does not consider semantic similarity or factual accuracy.
• ROUGE and BERTScore are often better suited for summarization tasks because they measure recall and contextual similarity rather than just n-gram precision.

BLEU remains useful for machine translation, where word-for-word similarity is more important. However, for summarization tasks, BLEU’s precision-based approach often fails to capture meaning, making ROUGE or BERTScore better choices.

Also Read: Machine Translation in NLP: Examples, Flow & Models

With the theory in place, it’s time to put the text summarization techniques in NLP into action. Let’s explore how you can code text summarization in a step-by-step process.

Coding Text Summarization: Step-by-Step Implementation

Coding text summarization is essential for automating information processing in real-time applications like news aggregation, legal document analysis, and customer service chatbots. 

Implementing these text summarization techniques in NLP in code helps developers to fine-tune models for specific datasets. They can optimize performance for large-scale data, and integrate summarization into complex AI pipelines, enhancing both speed and accuracy in decision-making processes.

Before diving into the implementation, ensure you have the necessary libraries installed. These libraries will assist with text processing, vectorization, and similarity scoring.

• NumPy: For numerical operations and array handling.
• NLTK (Natural Language Toolkit): For text preprocessing like tokenization, stop word removal, and stemming.
• GloVe (Global Vectors for Word Representation): For converting words into vector representations (embeddings).
• Scikit-learn: For vectorization and cosine similarity calculations.

Installation:

pip install numpy nltk sklearn

For GloVe embeddings, download pre-trained vectors from the GloVe website and load them into your project.

Here’s the step-by-step coding process, which demonstrates extractive summarization using a frequency-based approach combined with cosine similarity and TextRank for ranking:

1. Preprocessing the Text

Clean and prepare the text by tokenizing sentences, removing stop words, and normalizing words through stemming or lemmatization.

Code:

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import sent_tokenize, word_tokenize
from string import punctuation

# Download the necessary NLTK data
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('punkt_tab') # This line downloads the missing data

text = """
Natural Language Processing (NLP) focuses on the interaction between computers and humans through natural language. Text summarization is a crucial task in NLP, enabling efficient data consumption. There are two main types of summarization: extractive and abstractive. Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences to convey the original meaning.
"""

# Sentence tokenization
sentences = sent_tokenize(text)

# Removing stop words and punctuation
stop_words = set(stopwords.words('english'))
processed_sentences = [
    [word.lower() for word in word_tokenize(sentence) if word.lower() not in stop_words and word not in punctuation]
    for sentence in sentences
]

print(processed_sentences)

Explanation:

• sent_tokenize(text): Splits the text into individual sentences.
• word_tokenize(sentence): Breaks each sentence into words.
• Stop Words Removal: Removes common words like "the", "is", and punctuation which don’t contribute to summarization.
• Lowercasing: Converts all words to lowercase to standardize processing.

Output:

[['natural', 'language', 'processing', 'nlp', 'focuses', 'interaction', 'computers', 'humans', 'natural', 'language'], ['text', 'summarization', 'crucial', 'task', 'nlp', 'enabling', 'efficient', 'data', 'consumption'], ['two', 'main', 'types', 'summarization', 'extractive', 'abstractive'], ['extractive', 'summarization', 'selects', 'key', 'sentences', 'original', 'text', 'abstractive', 'summarization', 'generates', 'new', 'sentences', 'convey', 'original', 'meaning']]

2. Vectorization Using TF-IDF

Convert the cleaned text into numerical vectors to measure sentence importance.

Code:

from sklearn.feature_extraction.text import TfidfVectorizer

# Joining tokenized words into full sentences
processed_text = [' '.join(sentence) for sentence in processed_sentences]

# TF-IDF Vectorization
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(processed_text)

print(vectors.toarray())

Explanation:

• TfidfVectorizer(): Converts the preprocessed text into numerical vectors based on word importance.
• TF-IDF (Term Frequency-Inverse Document Frequency): Measures how important a word is in a sentence relative to the entire text.

Output:

[[0.         0.27094807 0.         0.         0.         0.
  0.         0.         0.         0.27094807 0.         0.27094807
  0.27094807 0.         0.54189613 0.         0.         0.54189613
  0.         0.21361857 0.         0.27094807 0.         0.
  0.         0.         0.         0.         0.        ]
 [0.         0.         0.36153669 0.         0.36153669 0.36153669
  0.36153669 0.36153669 0.         0.         0.         0.
  0.         0.         0.         0.         0.         0.
  0.         0.28503968 0.         0.         0.         0.
  0.23076418 0.36153669 0.28503968 0.         0.        ]
 [0.36559366 0.         0.         0.         0.         0.
  0.         0.         0.36559366 0.         0.         0.
  0.         0.         0.         0.46370919 0.         0.
  0.         0.         0.         0.         0.         0.
  0.29597957 0.         0.         0.46370919 0.46370919]
 [0.18849645 0.         0.         0.23908385 0.         0.
  0.         0.         0.18849645 0.         0.23908385 0.
  0.         0.23908385 0.         0.         0.23908385 0.
  0.23908385 0.         0.47816769 0.         0.23908385 0.47816769
  0.30520824 0.         0.18849645 0.         0.        ]]

Each row represents a sentence, and each column represents a unique word. The values indicate the importance of each word in a sentence relative to the full text.

3. Calculating Sentence Similarity
Use cosine similarity to determine how closely related sentences are, which helps in identifying key sentences for extraction.

Code:

from sklearn.metrics.pairwise import cosine_similarity

# Calculate cosine similarity between sentences
similarity_matrix = cosine_similarity(vectors)

print(similarity_matrix)

Explanation:

• Cosine Similarity: Measures how similar two sentences are by comparing the angle between their vector representations.
• A similarity score of 1 indicates identical sentences, while 0 indicates no similarity.

Output:

[[1.         0.06088977 0.         0.        ]
 [0.06088977 1.         0.06830148 0.1241601 ]
 [0.         0.06830148 1.         0.22816162]
 [0.         0.1241601  0.22816162 1.        ]]

Diagonal Values (1.0): Each sentence is perfectly similar to itself, which is expected in a similarity matrix.

Off-diagonal Values: These represent the similarity between different sentences. For example: 

• Sentence 2 and Sentence 4 have a similarity of 0.124.
• Sentence 3 and Sentence 4 have the highest off-diagonal similarity at 0.228.

4. Ranking Sentences Using TextRank

Apply the TextRank algorithm to rank sentences based on their relevance and importance.

Code:

import networkx as nx

# Build similarity graph
similarity_graph = nx.from_numpy_array(similarity_matrix)

# Apply TextRank (PageRank for text)
scores = nx.pagerank(similarity_graph)

# Rank sentences based on scores
ranked_sentences = sorted(((scores[i], s) for i, s in enumerate(sentences)), reverse=True)

for score, sentence in ranked_sentences:
    print(f"Score: {score:.4f} - {sentence}")

Explanation:

• NetworkX: Constructs a graph where nodes are sentences, and edges represent similarity scores.
• PageRank: Ranks sentences based on their importance in the graph—sentences more central to the content receive higher scores.

Output:

Score: 0.2585 - Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences to convey the original meaning.
Score: 0.2505 - Text summarization is a crucial task in NLP, enabling efficient data consumption.
Score: 0.2503 - There are two main types of summarization: extractive and abstractive.
Score: 0.2407 - 
Natural Language Processing (NLP) focuses on the interaction between computers and humans through natural language.

5. Extracting the Summary
Select the top-ranked sentences to form the final summary.

Code: 

# Extracting top 2 sentences for the summary
summary = ' '.join([ranked_sentences[i][1] for i in range(2)])
print("Summary:\n", summary)

Explanation:

• The top 2 sentences with the highest TextRank scores are selected to form the summary.
• This method ensures the summary includes the most important, content-rich sentences.

Output:

Summary:
Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences to convey the original meaning. Text summarization is a crucial task in NLP, enabling efficient data consumption.

The process identifies the most relevant sentences based on their contextual importance. You can adjust the number of sentences extracted to control summary length.

For more advanced summarization, abstractive techniques using transformer models like BERT and T5 can be implemented for more natural, human-like summaries.

Also Read: Top 9 Machine Learning APIs for Data Science You Need to Know About

While coding your own summarization models gives you control and flexibility, some powerful APIs and tools can simplify the process. Let’s explore some of the best options available and how they can be applied to different use cases.

Best APIs and Tools for Text Summarization: Features and Use Cases

APIs and tools for text summarization have made it easier than ever to integrate summarization capabilities into applications without building models from scratch.

These tools cater to diverse industries, from media and legal to healthcare and customer service, offering customizable solutions for both extractive and abstractive summarization needs.

By leveraging pre-trained models and scalable APIs, businesses can process large volumes of text efficiently, streamline workflows, and improve user experiences.

Here are some popular tools and their key features:

1. AssemblyAI’s Summarization Models

Features:

• Advanced speech-to-text summarization, ideal for transcribing and summarizing audio content like podcasts, meetings, and webinars.
• Supports real-time summarization with customizable verbosity levels.

Use Cases: Media companies summarizing interviews, educational platforms condensing lectures.

2. Microsoft Azure Text Analytics

Features:

• Offers extractive summarization via its Text Analytics API, with support for multiple languages.
• Integrated into the broader Azure ecosystem, enabling seamless deployment in large-scale enterprise applications.

Use Cases: Summarizing customer feedback, legal document processing, and automating business reports.

Also Read: How Does an Azure Virtual Network Work? Everything You Need to Know

3. MeaningCloud’s Automatic Summarization API

Features:

• Provides customizable extractive summarization with control over summary length and focus.
• Supports domain-specific tuning for better performance in specialized industries.

Use Cases: Market research firms summarizing reports, financial analysts condensing economic data.

4. NLP Cloud Summarization API

Features:

• Offers both extractive and abstractive summarization using models like GPT-J and T5.
• Allows fine-tuning for specific use cases and industries.

Use Cases: SaaS platforms integrating summarization features, healthcare providers condensing patient reports.

These APIs and tools simplify the integration of text summarization into diverse workflows, providing scalable solutions that cater to specific industry needs. 

Whether it's summarizing news articles, legal contracts, or customer reviews, these tools offer the flexibility and power needed for efficient information processing.

Also Read: 32+ Exciting NLP Projects GitHub Ideas for Beginners and Professionals in 2025

Using these tools can streamline summarization tasks, but they also come with their own set of challenges. Let’s explore the main obstacles involved in text summarization and how to navigate them effectively.

What Are the Benefits and Challenges of Text Summarization?

As industries grapple with ever-growing content—whether in news, research, or business reports—summarization helps streamline workflows, improve decision-making, and personalize content delivery. 

However, while the benefits are substantial, the implementation of summarization techniques comes with its own set of technical and practical challenges. 

From handling language complexity to ensuring grammatical accuracy, overcoming these obstacles is key to advancing NLP tools.

Here are some key benefits of text summarization in NLP:

Also Read: Deep Learning Vs NLP: Difference Between Deep Learning & NLP

Key Challenges in Text Summarization and Their Solutions

While the benefits are transformative, text summarization faces significant challenges, particularly when dealing with complex language structures, domain-specific content, and the intricacies of generating coherent, contextually accurate summaries. 

Addressing these challenges not only improves summarization models but also drives innovation across NLP applications.

Here’s a table shedding light on the key challenges and their solutions:

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

To fully overcome these challenges and leverage the benefits, gaining hands-on experience is crucial. Let’s explore how upGrad can help you develop practical skills in text summarization and NLP.

How Can upGrad Help You Learn Text Summarization in NLP?

upGrad, South Asia’s leading Higher EdTech platform offers comprehensive courses that equip over 10M+ learners with cutting-edge NLP skills, including text summarization techniques.

The courses focus on real-world case studies, industry projects, and essential NLP techniques, equipping you with the skills needed to apply NLP solutions in media, healthcare, finance, and more.

Here are some relevant courses you can check out:

• Introduction to Natural Language Processing
• Post Graduate Certificate in Machine Learning & NLP (Executive)
• Post Graduate Certificate in Machine Learning and Deep Learning (Executive)
• Executive Diploma in Machine Learning and AI with IIIT-B
• Executive Diploma in Data Science & AI

You can also get personalized career counseling with upGrad to guide your career path, or visit your nearest upGrad center and start hands-on training today!

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References:
https://thesocialshepherd.com/blog/twitter-statistics