Text Summarization

In previous lessons, you learned how machines translate text and how attention helps models focus on important words.

Now we apply the same idea to another powerful NLP task: Text Summarization.

Text summarization helps machines read long documents and produce shorter, meaningful summaries. This is widely used in news apps, search engines, research tools, and modern AI assistants.

What Is Text Summarization?

Text summarization is the task of condensing a long text into a shorter version while preserving the most important information.

The summary should:

• Be shorter than the original text
• Retain key ideas
• Remain readable and coherent

Humans do this naturally. NLP models try to automate this process.

Why Text Summarization Is Important

Modern systems deal with massive amounts of text:

• News articles
• Research papers
• Legal documents
• Customer reviews

Summarization helps:

• Save reading time
• Improve information retrieval
• Support decision-making

Main Types of Text Summarization

There are two primary approaches. Understanding this distinction is extremely important for exams and interviews.

Extractive Summarization

Extractive summarization works by selecting important sentences directly from the original text.

No new sentences are created. The summary is formed by choosing and combining existing sentences.

Example:

• Original: A long news article
• Summary: Top 3 important sentences from that article

This approach is simpler and more stable.

How Extractive Summarization Works

Typical steps:

1. Split text into sentences
2. Convert sentences into vectors
3. Score sentences based on importance
4. Select top-ranked sentences

Common techniques:

• TF-IDF
• TextRank
• Sentence embeddings

Abstractive Summarization

Abstractive summarization generates new sentences that may not appear in the original text.

It behaves more like a human:

• Understands meaning
• Rephrases content
• Uses its own vocabulary

This approach is more powerful but also more complex.

Abstractive Summarization Using Neural Models

Abstractive summarization is usually implemented using:

• Seq2Seq models
• Attention mechanisms
• Transformers (later lessons)

The model:

• Reads the entire document
• Understands context
• Generates a concise summary

Why Attention Is Critical for Summarization

In long documents, not all words are equally important.

Attention helps the model:

• Focus on key sentences
• Ignore irrelevant details
• Maintain context across paragraphs

Without attention, summaries tend to lose meaning.

High-Level Flow of Neural Summarization

The summarization pipeline looks like this:

1. Encoder reads the document
2. Attention identifies important regions
3. Decoder generates summary step by step

This process repeats for each word in the summary.

Conceptual Pseudocode (Summarization)

Practice Environment:

• Google Colab
• Jupyter Notebook

encoder_states = encoder(document)

decoder_state = init_state
summary = []

while not end_token:
    scores = attention(decoder_state, encoder_states)
    weights = softmax(scores)
    context = sum(weights * encoder_states)

    next_word, decoder_state = decoder(context, decoder_state)
    summary.append(next_word)

Extractive vs Abstractive: Key Differences

Real-World Applications

Text summarization is used in:

• News summarization apps
• Search engine snippets
• Research paper summaries
• Legal document analysis
• Email and chat summarization

Modern AI assistants rely heavily on summarization.

Assignment / Homework

Theory:

• Explain extractive vs abstractive summarization
• Why attention is needed for summarization

Practical:

• Implement an extractive summarizer using TF-IDF
• Summarize a news article

Environment:

• Google Colab
• Jupyter Notebook

Practice Questions

Quick Quiz

Quick Recap

• Text summarization condenses long documents
• Extractive selects existing sentences
• Abstractive generates new sentences
• Attention improves context understanding
• Summarization is widely used in real systems

Next lesson: Question Answering