Extractive summarization in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is extractive summarization?

Extractive summarization is a method that creates a summary by selecting important sentences or phrases directly from the original text without changing them.

beginner

How does extractive summarization differ from abstractive summarization?

Extractive summarization picks exact parts from the text, while abstractive summarization rewrites the content in new words to create a summary.

intermediate

Name a common technique used in extractive summarization.

One common technique is scoring sentences based on word frequency or importance, then selecting the top scoring sentences for the summary.

beginner

Why is extractive summarization easier to implement than abstractive summarization?

Because it only selects existing sentences without generating new text, it requires less complex language understanding and generation.

intermediate

What is a limitation of extractive summarization?

It may produce summaries that are less coherent or natural because it only copies parts of the original text without rewriting.

What does extractive summarization do?

A Translates the text into another language

B Generates new sentences to summarize the text

C Removes all stop words from the text

D Selects important sentences from the original text

Extractive summarization selects important sentences directly from the original text to form a summary.

Which of these is a common way to score sentences in extractive summarization?

A Using a random number generator

B Counting word frequency

C Translating sentences to another language

D Replacing words with synonyms

Counting word frequency helps identify important sentences by how often key words appear.

Why might extractive summaries be less natural?

A They translate text incorrectly

B They use too many new words

C They copy sentences without rewriting

D They remove all punctuation

Extractive summaries copy sentences as-is, which can make the summary less smooth or coherent.

Which summarization method rewrites content in new words?

A Abstractive summarization

B Extractive summarization

C Keyword extraction

D Text translation

Abstractive summarization generates new sentences to express the main ideas.

Extractive summarization is easier to implement because:

A It does not generate new text

B It requires complex language models

C It translates text automatically

D It summarizes by rewriting sentences

Extractive summarization only selects existing sentences, so it needs less complex processing.

Explain extractive summarization and how it works in simple terms.

Think about picking key sentences like highlighting important parts in a book.

List advantages and disadvantages of extractive summarization.

Consider what is easy and what might feel unnatural about copying text directly.

Practice

1. What is the main goal of extractive summarization in NLP?

easy

A. To translate the text into another language

B. To rewrite the text using simpler words

C. To select important sentences from the original text to create a summary

D. To generate new sentences that explain the text

Solution

1. Step 1: Understand extractive summarization

   Extractive summarization picks key sentences directly from the original text without changing them.

2. Step 2: Compare options

   Only To select important sentences from the original text to create a summary describes selecting important sentences from the original text, which matches extractive summarization.

3. Final Answer:

   To select important sentences from the original text to create a summary -> Option C

4. Quick Check:

   Extractive summarization = selecting key sentences [OK]

Hint: Extractive means picking from original text directly [OK]

Common Mistakes:

• Confusing extractive with abstractive summarization
• Thinking it rewrites or translates text
• Assuming it generates new sentences

2. Which of the following is a common technique used in extractive summarization?

easy

A. Neural machine translation

B. Text generation with GPT

C. Part-of-speech tagging

D. TF-IDF scoring of sentences

Solution

1. Step 1: Identify techniques for extractive summarization

   Extractive summarization often uses TF-IDF to score sentences by importance based on word frequency.

2. Step 2: Eliminate unrelated options

   Neural machine translation and text generation are for other NLP tasks, and POS tagging is not directly used for summarization scoring.

3. Final Answer:

   TF-IDF scoring of sentences -> Option D

4. Quick Check:

   TF-IDF = common extractive technique [OK]

Hint: TF-IDF ranks sentence importance in extractive summarization [OK]

Common Mistakes:

• Confusing summarization with translation or generation
• Thinking POS tagging directly creates summaries
• Ignoring TF-IDF's role in scoring

3. Given the following Python code snippet using TF-IDF for extractive summarization, what will be the output?

from sklearn.feature_extraction.text import TfidfVectorizer

texts = ["Cats are great pets.", "Dogs are loyal animals.", "Cats and dogs can live together."]
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(texts)
scores = X.sum(axis=1)
print(scores)

medium

A. [[0.0], [0.0], [0.0]]

B. [[2.0], [2.0], [2.4]]

C. [[2.0], [2.0], [3.0]]

D. [[1.0], [1.0], [1.0]]

Solution

1. Step 1: Understand TF-IDF vectorization and summing

   The code vectorizes three sentences and sums TF-IDF scores per sentence (row-wise sum).

2. Step 2: Calculate approximate sums

   Each sentence has TF-IDF scores summing roughly to 2.0, 2.0, and 2.4 respectively due to shared and unique words.

3. Final Answer:

   [[2.0], [2.0], [2.4]] -> Option B

4. Quick Check:

   Sum TF-IDF per sentence ≈ [[2.0], [2.0], [2.4]] [OK]

Hint: Sum TF-IDF scores per sentence to get importance [OK]

Common Mistakes:

• Assuming zero scores for all sentences
• Confusing sum with average
• Misunderstanding TF-IDF output shape

4. You have this extractive summarization code snippet:

sentences = ["AI is fascinating.", "It helps solve problems.", "AI can learn from data."]
scores = [0.8, 0.9, 0.85]
summary = []
for i in range(len(sentences)):
    if scores[i] > 0.85:
        summary.append(sentences[i])
print(summary)

What is the output and is there any bug?

medium

A. ['It helps solve problems.'] with no bug

B. ['AI is fascinating.', 'It helps solve problems.', 'AI can learn from data.'] with no bug

C. ['It helps solve problems.', 'AI can learn from data.'] but index error bug

D. [] because scores are not compared correctly

Solution

1. Step 1: Check score filtering condition

   The code adds sentences with scores > 0.85, so sentences with 0.9 and 0.85 are checked; 0.85 is not > 0.85, so only 0.9 and 0.85 fail or pass accordingly.

2. Step 2: Determine which sentences are included

   Scores: 0.8 (no), 0.9 (yes), 0.85 (no). So only "It helps solve problems." is included. But 0.85 is not > 0.85, so excluded.

3. Final Answer:

   ['It helps solve problems.'] -> Option A

4. Quick Check:

   Scores > 0.85 filter sentences correctly [OK]

Hint: Check strict > vs >= in score filtering [OK]

Common Mistakes:

• Including sentences with score equal to threshold
• Expecting index errors where none exist
• Misreading the comparison operator

5. You want to create an extractive summarizer that picks the top 2 sentences from a document based on TF-IDF scores. Given these sentences and their scores:

sentences = ["Machine learning is fun.", "It allows computers to learn.", "Summarization helps understand text.", "TF-IDF ranks sentence importance."]
scores = [0.7, 0.9, 0.6, 0.8]

Which two sentences should your summarizer select?

hard

A. ["It allows computers to learn.", "TF-IDF ranks sentence importance."]

B. ["Machine learning is fun.", "Summarization helps understand text."]

C. ["Summarization helps understand text.", "TF-IDF ranks sentence importance."]

D. ["Machine learning is fun.", "It allows computers to learn."]

Solution

1. Step 1: Identify top 2 scores

   The scores are 0.7, 0.9, 0.6, 0.8. The top two are 0.9 and 0.8.

2. Step 2: Match scores to sentences

   0.9 corresponds to "It allows computers to learn.", 0.8 corresponds to "TF-IDF ranks sentence importance.".

3. Final Answer:

   ["It allows computers to learn.", "TF-IDF ranks sentence importance."] -> Option A

4. Quick Check:

   Top 2 scores = 0.9 and 0.8 sentences [OK]

Hint: Pick sentences with highest TF-IDF scores [OK]

Common Mistakes:

• Choosing sentences with lower scores
• Mixing up sentence-score pairs
• Selecting more or fewer than top 2