Summarization in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is Summarization in AI?

Summarization is the process where AI reads a long text and creates a shorter version that keeps the main ideas.

beginner

What are the two main types of summarization?

The two main types are extractive (picking important sentences from the text) and abstractive (creating new sentences to explain the main ideas).

beginner

How does extractive summarization work?

It selects key sentences or phrases directly from the original text to form a summary without changing the words.

intermediate

What is a challenge of abstractive summarization?

It needs to understand the text deeply to create new sentences, which can sometimes lead to mistakes or missing details.

beginner

Why is summarization useful in real life?

It helps save time by giving quick overviews of long articles, reports, or conversations, making information easier to understand.

Which type of summarization picks sentences directly from the text?

A Predictive summarization

B Abstractive summarization

C Extractive summarization

D Generative summarization

Extractive summarization selects important sentences from the original text without changing them.

What does abstractive summarization do?

A Creates new sentences to explain ideas

B Copies sentences exactly

C Deletes unimportant words only

D Translates text to another language

Abstractive summarization generates new sentences that capture the main ideas in a simpler way.

Which is a common use of summarization?

A Making long texts shorter

B Increasing text length

C Changing text language

D Encrypting text

Summarization helps by making long texts shorter and easier to understand.

What is a risk when using abstractive summarization?

A Copying text exactly

B Missing important details

C Only selecting sentences

D Ignoring the main ideas

Abstractive summarization can sometimes miss details or make mistakes because it creates new sentences.

Which AI model type is often used for abstractive summarization?

A K-means clustering

B Decision trees

C Linear regression

D Sequence-to-sequence models

Sequence-to-sequence models are commonly used to generate new sentences in abstractive summarization.

Explain the difference between extractive and abstractive summarization.

Think about whether the summary uses original sentences or new ones.

Describe a real-life situation where summarization can save time.

Imagine you have a long email or news article to read fast.

Practice

1. What is the main purpose of text summarization in AI?

easy

A. To count the number of words in a text

B. To translate text into another language

C. To generate new text from scratch

D. To make long text shorter and easier to understand

Solution

1. Step 1: Understand the goal of summarization

   Summarization aims to reduce the length of text while keeping the main ideas clear.

2. Step 2: Compare options with the goal

   Only To make long text shorter and easier to understand describes making text shorter and easier to understand, which matches summarization.

3. Final Answer:

   To make long text shorter and easier to understand -> Option D

4. Quick Check:

   Summarization = shorten text [OK]

Hint: Summarization shortens text for quick understanding [OK]

Common Mistakes:

• Confusing summarization with translation
• Thinking summarization creates new text
• Mixing summarization with word counting

2. Which of the following is the correct way to call a summarization model in Python using a fictional API?

easy

A. summary = model.summarize(text)

B. summary = model.translate(text)

C. summary = model.generate(text)

D. summary = model.count_words(text)

Solution

1. Step 1: Identify the function for summarization

   The function to get a summary should be named something like 'summarize' to match the task.

2. Step 2: Match function names to tasks

   Only 'model.summarize(text)' fits the summarization task; others do translation, generation, or counting.

3. Final Answer:

   summary = model.summarize(text) -> Option A

4. Quick Check:

   Summarize function call = summary = model.summarize(text) [OK]

Hint: Look for 'summarize' function for summarization calls [OK]

Common Mistakes:

• Using translate() instead of summarize()
• Using generate() which creates new text
• Using count_words() which is unrelated

3. Given the code below, what will be the output?

text = "AI helps us by making complex tasks easier."
summary = model.summarize(text)
print(summary)

Assuming the model works correctly, what is the likely output?

medium

A. "AI simplifies complex tasks."

B. "AI translates text."

C. "AI helps us by making complex tasks easier."

D. "AI counts words in text."

Solution

1. Step 1: Understand summarization output

   The summary should be a shorter version of the original text keeping the main idea.

2. Step 2: Compare options to expected summary

   "AI simplifies complex tasks." shortens the sentence while keeping meaning; "AI helps us by making complex tasks easier." is original text, others unrelated.

3. Final Answer:

   "AI simplifies complex tasks." -> Option A

4. Quick Check:

   Summary shortens text = "AI simplifies complex tasks." [OK]

Hint: Summary is shorter but keeps main idea [OK]

Common Mistakes:

• Thinking summary is the same as original text
• Confusing summarization with translation
• Expecting unrelated outputs like word count

4. The following code throws an error. What is the likely cause?

text = "Summarize this text."
summary = model.summarize_text(text)
print(summary)

medium

A. The variable 'text' is not defined

B. The method name 'summarize_text' is incorrect

C. The print statement is missing parentheses

D. The model object is not created

Solution

1. Step 1: Check method name correctness

   The correct method to summarize is likely 'summarize', not 'summarize_text'.

2. Step 2: Verify other code parts

   The variable 'text' is defined, print has parentheses, and model object assumed created.

3. Final Answer:

   The method name 'summarize_text' is incorrect -> Option B

4. Quick Check:

   Method name must be correct = The method name 'summarize_text' is incorrect [OK]

Hint: Check method names carefully for typos [OK]

Common Mistakes:

• Assuming variable 'text' is undefined
• Forgetting print needs parentheses
• Ignoring if model object exists

5. You want to summarize a long article but keep important keywords intact. Which approach is best?

hard

A. Use translation model to convert text language

B. Use generative summarization to rewrite text freely

C. Use extractive summarization to select key sentences

D. Use word count to find important words

Solution

1. Step 1: Understand extractive vs generative summarization

   Extractive picks actual sentences from text, preserving keywords; generative rewrites freely.

2. Step 2: Choose method to keep keywords intact

   Extractive summarization keeps original sentences and keywords, so it fits the need best.

3. Final Answer:

   Use extractive summarization to select key sentences -> Option C

4. Quick Check:

   Keep keywords = extractive summarization [OK]

Hint: Extractive keeps original words; generative rewrites [OK]

Common Mistakes:

• Confusing generative with extractive summarization
• Using translation instead of summarization
• Relying on word count alone for keywords