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

# ROUGE

> Evaluates automatic summarization by measuring the longest common subsequence (LCS) that preserves the word order between candidate and reference summaries.

The ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric is one of the [Deterministic Metric](/concepts/metric) Galtea exposes to evaluate how well a generated summary captures the content of a reference summary. It is primarily used for summarization tasks and other scenarios where recall is more important than exact lexical match.

## Evaluation Parameters

To compute the `rouge` metric, the following parameters are required:

* **`actual_output`**: The model’s generated summary.
* **`expected_output`**: The reference (or gold) summary to compare against.

## How Is It Calculated?

This implementation uses **ROUGE-L**, which focuses on the **Longest Common Subsequence (LCS)** between the candidate and reference:

1. **Longest Common Subsequence**\
   Identifies the longest sequence of words that appears in both candidate and reference (not necessarily contiguous, but in the same order).

2. **Precision & Recall**
   * **Precision (P)** = LCS length / candidate length
   * **Recall (R)** = LCS length / reference length

3. **F1 Score**\
   Combines precision and recall:
   $$
   F1 = 2 \cdot \frac{P \times R}{R+P} 
   $$

ROUGE-L returns a score between **0 and 1**:

* **≥ 0.5** – Strong overlap with the reference summary.
* **0.3 – 0.5** – Moderate overlap; acceptable for abstractive summarization.
* **\< 0.3** – Weak overlap; likely missing key content.

## Suggested Test Case Types

Use ROUGE when evaluating:

* **Abstractive or extractive summaries**.
* **Headline generation** where recall of important tokens matters.
* **Content coverage tests** for long text generation.