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πŸ’Ό Applications🟒 Text Summarization

Text Summarization

🟒 This article is rated easy
Reading Time: 6 minutes
Last updated on March 10, 2025

Sander Schulhoff

Takeaways
  • Learn how AI can help you understand and simplify complex texts
  • Discover how to get summaries that match your needs, whether you're a student, professional, or casual reader
  • Master simple techniques for breaking down and organizing information from any text

Have you ever faced a long, complicated document and wished you had a smart assistant to help you understand it? That's exactly what AI tools like ChatGPT can do! These tools are like having a helpful friend who's really good at reading and explaining things in ways that make sense to you.

What Can AI Do With Text?

  • Summarizing books, articles, and documents
  • Explaining complex ideas in simple terms
  • Creating organized outlines
  • Adapting explanations for different audiences
  • Breaking down technical jargon

Getting Simple Summaries

Let's start with something simple. Say you want to understand Shakespeare's "Romeo and Juliet" but don't want to read all 24,000 words. Here's how to ask:

Astronaut

Basic summary request


Give me an act-by-act summary of Romeo and Juliet, keeping it simple and clear

Robot

AI Output


Here's Romeo and Juliet broken down into simple parts:


Act 1: Two families in Verona (the Montagues and Capulets) hate each other. Romeo Montague crashes a party at the Capulet house, where he falls in love with Juliet Capulet. The problem? Their families are enemies!


Act 2: Romeo and Juliet meet secretly in a garden. They're so in love that they decide to get married! A friendly priest named Friar Laurence agrees to help them marry in secret.


Act 3: Things go wrong. Romeo gets into trouble and has to leave town. Juliet is heartbroken and her parents don't understand why.


Act 4: Juliet gets help from Friar Laurence, who gives her a special potion to make everyone think she's dead (but she'll just be sleeping). This way, she can avoid marrying someone else and escape with Romeo.


Act 5: The plan fails because Romeo doesn't get the message explaining the trick. He thinks Juliet is really dead and takes poison. When Juliet wakes up and finds Romeo dead, she takes her own life. Their deaths finally make their families stop fighting.

Here's a simple prompt to get a summary of the text:

Astronaut

Basic Template


Summarize this text: [PASTE TEXT HERE]

Customized Analysis Techniques

The real power of LLMs lies in their ability to adapt content for different purposes and audiences. You can specify various parameters:

  1. Audience level

    • Technical vs. simplified explanations
    • Age-appropriate content
    • Domain-specific interpretations
  2. Output format

    • Executive summaries
    • Detailed analysis
    • Comparative studies
    • Domain-specific interpretations (e.g., sports analogies)
  3. Focus areas

    • Plot analysis
    • Character development
    • Thematic elements
    • Technical concepts

Example: Domain-Specific Interpretations

For example, if you're a football fan, you could ask ChatGPT:

Astronaut

Domain-specific interpretations


Give me a 100-word summary of "Romeo and Juliet" in American football terms.

Example: Audience Level

You can ask ChatGPT to summarize or create an outline of the text and TL;DR the whole thing. All you have to do is copy and paste the specific text into the prompt. You can include up to 2,500 words in each prompt. For longer works, break the text into smaller chunks.

Ask for a simplified summary of the chosen text by writing:

Astronaut

Audience level


Summarize this for me like I'm 5 years old: [PASTE TEXT HERE]

This one is a summary "for a 5-year-old" from a section of Wikipedia's entry on "quantum entanglement":

Astronaut

Prompt


Summarize this for me like I'm 5 years old:


Quantum entanglement is the phenomenon of a group of particles being generated, interacting, or sharing spatial proximity in such a way that the quantum state of each particle of the group cannot be described independently of the state of the others, including when the particles are separated by a large distance. The topic of quantum entanglement is at the heart of the disparity between classical and quantum physics: entanglement is a primary feature of quantum mechanics not present in classical mechanics.

Robot

AI Output


Quantum entanglement is a very strange thing that happens to very tiny things called particles. When two particles are "entangled," it means that they are connected in a special way, even if they are far apart. Scientists have found that if they measure something about one entangled particle, it can tell them something about the other one, even if they are very far apart. This is really weird and it took a long time for scientists to understand it. They used to think it was impossible, but now they know it's real! Even though it seems like this could be used to send messages really fast, it can't. But scientists are still studying it to see what they can learn from it.

Making Information Easier to Find

When you're dealing with any kind of text, it helps to organize the information. Here's how AI can help:

  1. Ask for categories

    • Main ideas
    • Important dates
    • Key people
    • Interesting facts
  2. Request different formats

    • Timeline
    • Bullet points
    • Question and answer
    • Step-by-step guide
  3. Get specific information

    • "What are the three most important points?"
    • "What changed between time period A and B?"
    • "How does this affect everyday life?"

Example: Structured Information Extraction

LLMs can create detailed outlines that reveal the organizational structure of texts. This is particularly useful for:

  • Research papers
  • Technical documentation
  • Complex narratives
  • Historical accounts

Example prompt for outline generation:

Astronaut

Structured information extraction


Create a detailed outline of the following text, including:

  • Main topics and subtopics
  • Key supporting points
  • Chronological or logical relationships

[PASTE TEXT]

The following is ChatGPT's outline on the Wikipedia page on "rope," specifically on the history of rope:

Robot

AI Output


I. Introduction

  • The use of ropes dates back to prehistoric times
  • The earliest ropes were naturally occurring lengths of plant fibre
  • The evolution of rope-making involved the twisting and braiding of plant fibres together
  • The earliest evidence of suspected rope was a fragment of three-ply cord found in a Neanderthal site

II. Historical development of rope-making

  • A 40,000-year-old tool found in Germany was identified in 2020 as a tool for making rope
  • The ancient Egyptians developed special tools to make rope around 4000-3500 BC
  • Other ancient civilisations used ropes made from various materials such as flax, grass, and animal hair
  • Rope-making spread throughout Asia, India, and Europe over the next several thousand years
  • From the Middle Ages until the 18th century, ropes were constructed in ropewalks, where strands of rope were laid out and then twisted together to form the rope
  • Domenico Fontana erected the 327-ton obelisk on Rome's Saint Peter's Square in 1586 with the help of 900 men, 75 horses, and countless pulleys and meters of rope

III. Modern developments in rope-making

  • Synthetic fibres like nylon and polypropylene have become increasingly popular since the 1950s
  • Nylon was first introduced into fiber ropes during World War II, and was used to make small braided parachute cords and three-strand tow ropes for gliders

IV. Conclusion

  • While synthetic fibres have largely replaced natural fibres in modern rope-making, the evolution of rope-making from prehistoric times to the present day has allowed for the creation of stronger and more durable ropes, which have played a crucial role in human history.

Best Practices for Text Analysis

  1. Verify information

    • Cross-reference important facts
    • Be aware of potential hallucinations
    • Check dates and specific claims
  2. Iterative refinement

    • Start with broad summaries
    • Follow up with specific questions
    • Request clarification on complex points
  3. Documentation

    • Keep track of source materials
    • Note any modifications or interpretations
    • Document prompt strategies that work well

As you become more familiar with these tools, you can develop more sophisticated strategies for your specific needs.

By languagenoodle

Sander Schulhoff

Sander Schulhoff is the Founder of Learn Prompting and an ML Researcher at the University of Maryland. He created the first open-source Prompt Engineering guide, reaching 3M+ people and teaching them to use tools like ChatGPT. Sander also led a team behind Prompt Report, the most comprehensive study of prompting ever done, co-authored with researchers from the University of Maryland, OpenAI, Microsoft, Google, Princeton, Stanford, and other leading institutions. This 76-page survey analyzed 1,500+ academic papers and covered 200+ prompting techniques.