Summarization in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main difference between extractive and abstractive summarization?

Summarization methods can be broadly categorized into extractive and abstractive. Which statement correctly describes their main difference?

A Extractive summarization selects key sentences from the original text, while abstractive summarization generates new sentences that capture the meaning.

B Extractive summarization generates new sentences, while abstractive summarization copies sentences directly from the text.

C Both extractive and abstractive summarization only select sentences without generating new text.

D Abstractive summarization only shortens text by removing stopwords, extractive summarization rewrites the text.

💡 Hint

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

Extractive summarization picks important sentences from the original text unchanged. Abstractive summarization rewrites or generates new sentences to convey the main ideas.

❓ Predict Output

intermediate

What is the output of this simple extractive summarization code?

Given the following Python code that extracts the first sentence as a summary, what will be printed?

Prompt Engineering / GenAI

text = "Machine learning helps computers learn from data. It is widely used in AI. Summarization is one application."
summary = text.split('.')[0] + '.'
print(summary)

A It is widely used in AI.

B Machine learning helps computers learn from data. It is widely used in AI.

C Summarization is one application.

D Machine learning helps computers learn from data.

💡 Hint

Look at how the text is split and which part is selected.

The code splits the text at periods and selects the first part, then adds a period. So it prints the first sentence only.

❓ Model Choice

advanced

Which model architecture is best suited for abstractive summarization?

You want to build an abstractive summarization system that generates new sentences. Which model architecture is most appropriate?

A K-means clustering algorithm

B Simple feedforward neural network

C Sequence-to-sequence model with attention mechanism

D Decision tree classifier

💡 Hint

Consider models that can generate sequences from input sequences.

Sequence-to-sequence models with attention can generate new text based on input sequences, making them ideal for abstractive summarization.

❓ Metrics

advanced

Which metric is commonly used to evaluate summarization quality?

When evaluating how good a summary is compared to a reference summary, which metric is most commonly used?

A Accuracy

B ROUGE score

C Mean Squared Error

D F1 score for classification

💡 Hint

Think about metrics that compare overlap of words or phrases.

ROUGE measures overlap of n-grams between generated and reference summaries, making it standard for summarization evaluation.

🔧 Debug

expert

Why does this abstractive summarization model output repetitive phrases?

Consider a trained abstractive summarization model that often repeats the same phrase multiple times in its output. What is the most likely cause?

A The model's decoding method lacks a mechanism to prevent repetition, such as coverage or beam search diversity.

B The training data contains only repetitive phrases, so the model learned to repeat.

C The input text is too short, causing the model to repeat phrases.

D The model uses a feedforward network instead of a recurrent network.

💡 Hint

Think about how models generate sequences and avoid repeating themselves.

Repetitive output often happens when the decoding strategy does not include mechanisms like coverage or diversity to avoid repeating the same phrases.

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