> ## 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-L

> Measures summary quality by finding the longest common subsequence (LCS) of words, capturing sentence-level structural similarity without requiring consecutive matches.

### 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)**: Stronger structural similarity and correct word order
* **Lower scores (closer to 0)**: Weak structural similarity

## Formula

$$
\mathrm{ROUGE\text{-}L} = \frac{\text{Length of LCS}}{\text{Total number of words in the reference}}
$$

#### Example Calculation

* Reference: "the cat sat on the mat" (m = 6)
* Candidate: "the cat lies on the rug" (n = 6)
* LCS(X,Y) = \[the, cat, on, the] → length = 4

Compute ROUGE-L using the formula:

$$
\mathrm{ROUGE\text{-}L} = \frac{4}{6} \approx 0.667
$$

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

## Use Cases

* Evaluating abstractive summarization
* Assessing machine translation