Rouge 2

ROUGE-2: Bigram-Level Evaluation for Text Summarization

ROUGE-2 is a crucial evaluation metric used in text summarization to measure the bigram-level recall. It quantifies how many consecutive two-word sequences (bigrams) present in a human-written "reference" summary are also found in a "candidate" (automatically generated) summary.

What is ROUGE-2?

ROUGE-2's core functionality revolves around comparing bigrams, which are pairs of adjacent words. The metric assesses the overlap of these bigram pairs between the candidate summary and its corresponding reference summary. By focusing on bigrams, ROUGE-2 provides a more nuanced evaluation than ROUGE-1 (which focuses on unigrams), as it captures short phrase-level similarities and the sequential nature of language.

ROUGE-2 Formula

The ROUGE-2 score is calculated as follows:

$$ \text{ROUGE-2 Recall} = \frac{\text{Number of overlapping bigrams between candidate and reference summary}}{\text{Total number of bigrams in the reference summary}} $$

Example: ROUGE-2 Computation

Let's illustrate the ROUGE-2 calculation with an example:

Candidate Summary: Machine learning is seen as a subset of artificial intelligence.

Reference Summary: Machine Learning is a subset of artificial intelligence.

Step 1: Extract Bigrams

First, we extract all possible consecutive two-word sequences (bigrams) from both summaries. We'll ignore case and punctuation for simplicity in this example.

Candidate Summary Bigrams:

• machine learning

• learning is

• is seen

• seen as

• as a

• a subset

• subset of

• of artificial

• artificial intelligence

Reference Summary Bigrams:

• machine learning

• learning is

• is a

• a subset

• subset of

• of artificial

• artificial intelligence

Step 2: Count Overlapping Bigrams

Next, we identify the bigrams that are present in both the candidate and reference summaries.

Matching Bigrams:

• machine learning

• learning is

• a subset

• subset of

• of artificial

• artificial intelligence

• Total overlapping bigrams: 6

• Total bigrams in reference summary: 7

Step 3: Calculate ROUGE-2 Score

Finally, we apply the ROUGE-2 formula:

$$ \text{ROUGE-2 Recall} = \frac{6}{7} \approx 0.857 $$

Final Note

A ROUGE-2 recall score of approximately 0.857 (or 85.7%) in this example indicates that a high proportion of the bigrams from the reference summary were successfully captured by the candidate summary. This suggests that the generated summary closely reflects the local word order and phrasing of the original text. ROUGE-2 is valuable for assessing how well a summarization model preserves essential n-gram structures.

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Interview Questions

• What does ROUGE-2 measure in summary evaluation?

• How is the ROUGE-2 recall score calculated?

• Why are bigrams important in evaluating summaries?

• How does ROUGE-2 differ from ROUGE-1?

• Can ROUGE-2 capture phrase-level fluency better than ROUGE-1? Why?

• What does a ROUGE-2 score of 0.857 indicate?

• How do you extract bigrams from text for ROUGE-2 evaluation?

• What limitations might ROUGE-2 have in summary evaluation?

• How does ROUGE-2 handle reordered but semantically similar phrases?

• How can ROUGE-2 scores guide the improvement of summarization models?