> ## 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.

# METEOR

> Evaluates translation, summarization, and paraphrasing by aligning words using exact matches, stems, or synonyms, computing precision and recall.

The METEOR (Metric for Evaluation of Translation with Explicit ORdering) metric is one of the [Deterministic Metric](/concepts/metric) Galtea exposes for evaluating machine translation, summarization, and paraphrasing tasks. It aims to correlate more closely with human judgments compared to [BLEU](/concepts/metric/bleu).

## Evaluation Parameters

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

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

## How Is It Calculated?

METEOR improves upon [BLEU](/concepts/metric/bleu) by considering **semantic and morphological matches**:

1. **Alignment**\
   Tokens are matched between candidate and reference using:
   * **Exact matches**
   * **Stems** (e.g., "run" vs. "running")
   * **Synonyms** (e.g., "big" vs. "large")

2. **Precision & Recall**\
   Both are calculated from the aligned tokens.

3. **Fragmentation Penalty**\
   A penalty is applied if matches are scattered (fragmented alignment).

4. **Final Score**\
   The score is computed as:
   $$
   METEOR = F_{mean} \times (1 - Penalty)
   $$
   where F\_mean is the harmonic mean of precision and recall.

METEOR returns a score between **0 and 1**:

* **≥ 0.6** – High-quality translation/summary with semantic fidelity.
* **0.3 – 0.6** – Moderate quality; some paraphrasing or structural divergence.
* **\< 0.3** – Low-quality or semantically incorrect output.

## Suggested Test Case Types

Use METEOR when evaluating:

* **Machine Translation** with varied phrasings.
* **Abstractive Summarization** where synonyms are common.
* **Paraphrase Detection** with semantic variation.