# ROUGE score

> Supported in: Batch

Computes ROUGE precision, recall, and F-measure for predicted and target text. Text is lowercased; only ASCII letters and digits are compared. All other characters are treated as word separators. Returns null if either input is null or the text cannot be scored, including when it exceeds the scorer's size limits.

**Expression categories:** Distance measurement, String

## Declared arguments

* **Prediction:** The machine-generated text to evaluate.<br>*Expression\<String>*
* **ROUGE type:** The ROUGE variant used to compare the texts.<br>*Enum\<ROUGE-1, ROUGE-2, ROUGE-3, ROUGE-L, ROUGE-Lsum, ROUGE-Lsum (detected sentences)>*
* **Target:** The reference text to compare against.<br>*Expression\<String>*
* *optional* **Use stemmer:** Stem words before comparing them.<br>*Literal\<Boolean>*

**Output type:** *Struct\<precision:Double, recall:Double, fmeasure:Double>*

## Examples

### Example 1: Base case

**Description:** Only ASCII letters and digits are compared after lowercasing; other characters separate words. Text without any tokens scores zero, even when the inputs are identical.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge1`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| 你好 | 你好 | {<br> **fmeasure**: 0.0,<br> **precision**: 0.0,<br> **recall**: 0.0,<br>} |
| cat你好dog | cat dog | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |
| CAT 42 | cat 42 | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |
| *empty string* | *empty string* | {<br> **fmeasure**: 0.0,<br> **precision**: 0.0,<br> **recall**: 0.0,<br>} |

***

### Example 2: Base case

**Description:** ROUGE-2 measures overlap between adjacent pairs of words.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge2`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| the cat sat | the cat slept | {<br> **fmeasure**: 0.5,<br> **precision**: 0.5,<br> **recall**: 0.5,<br>} |

***

### Example 3: Base case

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge1`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| the cat sat | the cat sat | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |

***

### Example 4: Base case

**Description:** ROUGE-L measures the longest sequence of words appearing in the same order.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge_l`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| cat sat | sat cat | {<br> **fmeasure**: 0.5,<br> **precision**: 0.5,<br> **recall**: 0.5,<br>} |

***

### Example 5: Base case

**Description:** ROUGE-Lsum uses newlines to separate sentences; punctuation alone does not split them.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge_lsum`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| cat sat.<br>dog ran. | dog ran.<br>cat sat. | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |
| cat sat. dog ran. | dog ran. cat sat. | {<br> **fmeasure**: 0.5,<br> **precision**: 0.5,<br> **recall**: 0.5,<br>} |

***

### Example 6: Base case

**Description:** ROUGE-1 measures overlap between individual words.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge1`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| the cat sat | the cat slept | {<br> **fmeasure**: 0.6666666666666666,<br> **precision**: 0.6666666666666666,<br> **recall**: 0.6666666666666666,<br>} |

***

### Example 7: Base case

**Description:** Stemming allows related word forms to match.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge1`
* **Target:** `target`
* **Use stemmer:** true

| prediction | target | **Output** |
| ----- | ----- | ----- |
| cars | car | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |

***

### Example 8: Base case

**Description:** ROUGE-Lsum sentences automatically detects sentence boundaries and compares sentence-level matches without penalizing sentence reordering.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge_lsum_sentences`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| The dog ran. The cat sat. | The cat sat. The dog ran. | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |
| Cat sat. Dog ran. | Dog ran. Cat sat. | {<br> **fmeasure**: 1.0,<br> **precision**: 1.0,<br> **recall**: 1.0,<br>} |

***

### Example 9: Base case

**Description:** ROUGE-3 measures overlap between adjacent groups of three words.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge3`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| the cat sat down | the cat sat still | {<br> **fmeasure**: 0.5,<br> **precision**: 0.5,<br> **recall**: 0.5,<br>} |
| the cat | the cat | {<br> **fmeasure**: 0.0,<br> **precision**: 0.0,<br> **recall**: 0.0,<br>} |

***

### Example 10: Null case

**Description:** If either input is null, the entire ROUGE score output is null.

**Argument values:**

* **Prediction:** `prediction`
* **ROUGE type:** `rouge1`
* **Target:** `target`
* **Use stemmer:** *null*

| prediction | target | **Output** |
| ----- | ----- | ----- |
| *null* | target | *null* |
| prediction | *null* | *null* |
| *null* | *null* | *null* |

***