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.