30 GenAI in Banking & Finance : Understanding ROUGE Metrics: Evaluating Summarization Quality

Understanding ROUGE Metrics: Evaluating Summarization Quality

Evaluating the quality of machine-generated summaries is crucial in natural language processing, especially with the growing adoption of generative AI systems. One of the most widely used metrics for this purpose is ROUGE, which stands for Recall-Oriented Understudy for Gisting Evaluation. ROUGE scores help measure how closely a machine-generated summary matches human-created reference summaries.

How ROUGE Works

ROUGE operates by assessing the overlap of words, sequences of words, or even word pairs between the machine summary and the reference summaries. The assumption is simple: a good automated summary should capture key ideas present in human-written summaries.

Different types of ROUGE metrics exist—such as ROUGE-N (for n-gram overlap), ROUGE-L (based on longest common subsequence), and ROUGE-S (for skip-bigram overlap)—each capturing different aspects of textual similarity. In practice, multiple ROUGE variants are used together for a more comprehensive evaluation.

Key Considerations

When interpreting ROUGE results, two key aspects are often considered:

• Recall: Measures how much of the relevant information from the reference summaries appears in the generated summary. High recall means the summary captures most of the important points.
• Precision: Measures how much of the generated summary’s content is actually relevant or found in the reference summaries. High precision indicates that the model avoids including unnecessary or irrelevant details

Example in Practice

Consider a summarization model that is trained to summarize news articles. The reference (human-written) summary says:

“Company X launches a new smartphone with advanced features.”

The machine-generated summary says:

“Company X has launched a new smartphone with improved camera capabilities.”

Although both summaries discuss the same event, their word overlap differs slightly. ROUGE metrics quantify this difference. A high ROUGE score in this case would indicate significant overlap—both summaries identify the main idea: the smartphone launch. A lower score would suggest gaps in recall or precision, such as omitting key details or introducing unrelated ones.

# Install the library if not already installed
# !pip install rouge-score

from rouge_score import rouge_scorer

# Define the reference and machine-generated summaries
reference_summary = "Company X launches a new smartphone with advanced features."
generated_summary = "Company X has launched a new smartphone with improved camera capabilities."

# Initialize the ROUGE scorer for ROUGE-1, ROUGE-2, and ROUGE-L
scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)

# Calculate the ROUGE scores
scores = scorer.score(reference_summary, generated_summary)

# Display the scores
for rouge_type, score in scores.items():
    print(f"{rouge_type}: Precision={score.precision:.2f}, Recall={score.recall:.2f}, F1={score.fmeasure:.2f}")

rouge1: Precision=0.83, Recall=0.71, F1=0.77
rouge2: Precision=0.67, Recall=0.50, F1=0.57
rougeL: Precision=0.83, Recall=0.71, F1=0.77

These results mean:

• ROUGE-1 (unigram): The generated summary captures most key individual words from the reference, with good precision and recall, resulting in a strong F1 score.
• ROUGE-2 (bigram): There is less overlap when considering pairs of consecutive words, so the scores are slightly lower.
• ROUGE-L (longest common subsequence): This score, similar to ROUGE-1, reflects a good match in sequence structure between the summaries.

A high F1 score (such as 0.77 for ROUGE-1 and ROUGE-L) indicates strong similarity and summarizes the main ideas effectively. Lower scores (such as ROUGE-2's 0.57) often reflect differences in phrasing or omitted details. By analyzing these scores, developers can make targeted improvements to summarization model quality, balancing recall and precision for optimal results.

Why ROUGE Matters

ROUGE metrics play an essential role in developing and benchmarking text summarization systems. They provide a quantitative way to compare different models and track progress over time. However, while ROUGE captures surface-level similarity, it may not fully measure deeper aspects such as factual consistency, coherence, or readability—areas where complementary human evaluation or newer metrics like BERTScore can add value.

✍️ Author’s Note

This blog reflects the author’s personal point of view — shaped by 22+ years of industry experience, along with a deep passion for continuous learning and teaching.
The content has been phrased and structured using Generative AI tools, with the intent to make it engaging, accessible, and insightful for a broader audience.