Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
Extractive vs Abstractive Summarization in Azure AI Language
What is 'extractive vs abstractive summarisation' and which does Azure AI Language's document summarisation feature support?
Quick Answer
The correct answer is that Azure AI Language supports both extractive and abstractive summarization. Extractive summarization works by identifying and pulling the most important sentences directly from the source text, while abstractive summarization generates entirely new sentences that synthesize and rephrase the original content into a concise summary. This distinction matters because the Azure AI Language service offers both capabilities, giving you the flexibility to choose based on whether you need verbatim key points or a fluid, human-like restatement. On the AI-900 exam, this topic tests your understanding of natural language processing features within Azure AI, and a common trap is assuming the service only supports one type—remember, it provides both. For a quick memory tip, think of extractive as “extracting highlights” and abstractive as “abstracting the essence.”
⚠ Common exam trap
Many candidates assume abstractive summarization requires a separate service like Azure OpenAI, but Azure AI Language includes it natively, and they may also confuse the two types based on text length rather than the underlying technique.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation
Azure AI Language's document summarization feature supports both extractive summarization (selecting key sentences from the original text) and abstractive summarization (generating a new, condensed summary that rephrases the content). Option B is correct because the service provides both capabilities, allowing users to choose based on their needs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Azure AI Language only supports extractive summarisation — abstractive requires Azure OpenAI
Why it's wrong here
Azure AI Language supports both modes — abstractive summarisation is available as a pre-built feature in the Language service.
- ✓
Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation
Why this is correct
Both modes are available — extractive quotes source sentences; abstractive generates new text capturing the meaning.
- ✗
Azure AI Language only supports abstractive summarisation because it is more advanced
Why it's wrong here
Azure AI Language's document summarisation feature supports both extractive and abstractive summarisation, making the claim it *only* supports abstractive factually incorrect. The service provides both capabilities to cater to diverse use cases, where either direct quotation or rephrased content is required. This option is tempting because abstractive summarisation is indeed a more advanced technique, requiring natural language generation, which might suggest a cutting-edge AI service would exclusively focus on this sophisticated method. However, offering both methods provides greater flexibility and utility.
- ✗
Extractive is for short texts; abstractive is required for documents longer than 10,000 words
Why it's wrong here
Document length doesn't determine which mode to use — both can handle documents of various lengths.
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Azure Machine Learning Studio
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
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Variation 1. What is 'abstractive summarization' vs. 'extractive summarization' in Azure AI Language, and which produces summaries in new words?
medium- A.Extractive produces new words; abstractive copies sentences
- ✓ B.Abstractive generates new sentences; extractive selects existing sentences from the source
- C.They produce identical output through different computational paths
- D.Abstractive works only for legal documents; extractive for general text
Why B: Abstractive summarization generates new sentences that rephrase the core meaning of the source text, similar to how a human would summarize. Extractive summarization, in contrast, selects and copies key sentences directly from the original document without rewording them. Option B correctly identifies that abstractive produces new sentences while extractive selects existing ones.
Variation 2. What is the difference between extractive summarization and abstractive summarization?
medium- A.Extractive works on text; abstractive works on images
- ✓ B.Extractive pulls existing sentences; abstractive generates new text capturing the meaning
- C.Extractive is for long documents; abstractive is for short text
- D.Extractive summarization is always less accurate than abstractive
Why B: Extractive summarization identifies and extracts the most important sentences directly from the source text, while abstractive summarization generates new sentences that capture the core meaning, often using natural language generation techniques. In Azure AI Language, extractive summarization returns a set of ranked sentences from the original document, whereas abstractive summarization produces a concise summary that may rephrase content. This distinction is fundamental to understanding how different NLP workloads handle text summarization tasks.
Variation 3. What is 'abstractive summarisation' and how does it differ from 'extractive summarisation'?
medium- A.Extractive writes shorter summaries; abstractive writes longer ones
- ✓ B.Extractive selects key sentences verbatim; abstractive generates new sentences capturing the meaning
- C.Abstractive summarisation is only available for non-English languages
- D.Extractive summarisation uses generative AI; abstractive uses keyword ranking
Why B: Extractive summarisation works by selecting and concatenating the most important sentences directly from the source text, while abstractive summarisation uses natural language generation (NLG) models to produce entirely new sentences that paraphrase and condense the core meaning. This distinction is fundamental in Azure AI Language's summarisation capabilities, where extractive returns verbatim excerpts and abstractive generates novel, coherent summaries.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.