Text Summarization Techniques

 Text Summarization Techniques


We live in an age of information overload — news articles, research papers, blogs, and reports are everywhere. Reading everything word-for-word isn’t practical. That’s why Text Summarization is so useful. It’s a Natural Language Processing (NLP) technique that automatically creates shorter versions of text while preserving the main meaning.


Two Main Approaches to Text Summarization

1. Extractive Summarization


The system picks out the most important sentences or phrases directly from the original text.


It doesn’t generate new words, only selects key parts.


👉 Example:

Original Text: “Quantum computing has the potential to solve complex problems faster than classical computers. However, building stable quantum machines is still a challenge.”

Extractive Summary: “Quantum computing solves complex problems faster but building stable machines is a challenge.”


🔹 Common methods: frequency-based word scoring, graph-based algorithms (like TextRank).


2. Abstractive Summarization


The system generates a new summary using its own words.


It tries to mimic how humans write summaries, often requiring deep learning models.


👉 Example:

Original Text: “Quantum computing has the potential to solve complex problems faster than classical computers. However, building stable quantum machines is still a challenge.”

Abstractive Summary: “Quantum computers are powerful but hard to build.”


🔹 Common methods: sequence-to-sequence models, transformers (like BART, T5, GPT).


Other Techniques and Variations


Single-Document Summarization → Summarizes one article or report.


Multi-Document Summarization → Summarizes information from multiple sources.


Headline Generation → Creates a short title or headline instead of a paragraph.


Real-World Applications of Text Summarization


News Apps → Generate quick summaries of long articles.


Search Engines → Provide snippets under search results.


Academic Research → Summarize long scientific papers.


Customer Support → Summarize chat conversations or complaint logs.


✅ In short:


Extractive summarization = picking important sentences.


Abstractive summarization = rewriting the main idea in new words.


Both help save time and make information easier to digest.

Learn Artificial Intelligence Course in Hyderabad

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