A paragraph must become one sentence. Which model task fits?
Why D is the answer
Summarization
Shortening text while keeping meaning is summarization.
In detail
- Shortening text while keeping its meaning is summarization.
- Name the task first, because it guides the model and the prompt.
Use case
An illustrative scenario, not a real incident.
A news app needs one sentence for each article. That is summarization: shorter text that keeps the meaning. No label or entity is being extracted.
See it in different ways
flowchart LR S0["Long article"] --> S1["Model"] --> S2["One sentence"]
flowchart TD
Q{"What should come out?"}
Q -->|"A shorter version"| A0["Summarization"]
Q -->|"A label"| A1["Classification"]
Q -->|"Names and places"| A2["Entity recognition"]Key point: Shorter text with the same meaning is summarization.
Why the other options are wrong
- A. Text classification
- Classification assigns a label.
- B. Named entity recognition
- Entity recognition finds names and places.
- C. Token classification
- Token classification labels each token.