> ## Documentation Index
> Fetch the complete documentation index at: https://www.getmaxim.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# ROUGE-2

> Measures summary quality and local fluency by calculating the overlap of bigrams (word pairs) between the generated and reference texts. It is more stringent than ROUGE-1.

### Input

* **`output`** (str): The generated text
* **`expectedOutput`** (str): The reference text

### Output

* **`Result`** (float): A score between 0 and 1.

## Interpretation

* **Higher scores (closer to 1)**: Better bigram overlap (captures local word order and fluency)
* **Lower scores (closer to 0)**: Poor bigram overlap

## Formula

$$
\mathrm{ROUGE\text{-}2} = \frac{\text{Count of overlapping bigrams}}{\text{Total bigrams in reference}}
$$

#### Example Calculation

* Reference bigrams: \[the cat, cat sat, sat on, on the, the mat]
* Candidate bigrams: \[the cat, cat sits, sits on, on the, the mat]
* Overlapping bigrams: \[the cat, on the, the mat] = 3
* Total reference bigrams = 5

$$
\mathrm{ROUGE\text{-}2} = \frac{3}{5} = 0.6
$$

<Note>This is a **Similarity** Metric</Note>

## Use Cases

* Evaluating text summarization where sentence structure matters
* Assessing machine translation quality