Which Features Are Available in Azure AI Language's Extractive Summarization?
A team is developing a solution to automatically summarize long documents using Azure AI Language. Which feature should they use?
⚠ Common exam trap
Candidates often confuse key phrase extraction (which finds important words) with extractive summarization (which extracts entire sentences), leading them to choose Option B instead of the correct feature for document summarization.
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
✓
Extractive summarization.
Extractive summarization is the correct feature because it specifically identifies and extracts the most important sentences from a document to create a concise summary. Azure AI Language's extractive summarization uses a ranking model to score sentences based on relevance and informativeness, directly addressing the requirement to automatically summarize long documents.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sentiment analysis.
Why it's wrong here
Sentiment analysis returns polarity scores per document or sentence, producing no condensed summary text. It is tempting because it also consumes long documents, but the summarisation feature is what generates shortened abstractive or extractive output; sentiment only classifies opinion, so it cannot satisfy the requirement.
- ✗
Key phrase extraction.
Why it's wrong here
Key phrase extraction returns a list of salient terms, not coherent sentences summarising the document. It is tempting because it also reduces text to its important content, but summarisation requires the dedicated summarisation feature, which produces abstractive or extractive sentence output rather than isolated phrases.
- ✓
Extractive summarization.
Why this is correct
Extractive summarization selects and returns the most salient existing sentences from the source document verbatim, which suits condensing long documents without generating new wording. Abstractive summarization would paraphrase instead, risking factual drift from the original text.
- ✗
Entity recognition.
Why it's wrong here
Entity recognition extracts people, places and organisations; it does not condense document content into a shorter form. It is tempting because it also processes long text, but summarisation requires the dedicated summarisation feature, which returns abstractive or extractive sentence output rather than labelled entities.
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