You need to compare the quality of generated summaries against reference summaries. Which metric is meant for this?
Why C is the answer
ROUGE
ROUGE compares generated text with references. The others are for numeric or classification models.
In detail
- ROUGE compares generated summaries with reference summaries.
- ROUGE needs reference text.
Use case
An illustrative scenario, not a real incident.
A news team compares generated summaries with human ones using ROUGE, which scores overlap with the reference.
See it in different ways
flowchart TD
A{"Which error hurts most?"} -->|"False alarms"| B["Precision"]
A -->|"Missed cases"| C["Recall"]
A -->|"Both, with imbalanced classes"| D["F1 score"]
A -->|"Numeric predictions"| E["RMSE"]mindmap
root((Metrics))
Classification
Recall: catch every case
Precision: avoid false alarms
Regression
RMSE
Text
ROUGEflowchart LR S0["Generated summary"] --> S1["Reference summary"] --> S2["ROUGE score"]
Key point: ROUGE compares generated and reference text.
Why the other options are wrong
- A. R-squared
- R-squared is for regression.
- B. RMSE
- RMSE is for regression.
- D. AUC
- AUC is for classification.