> ## 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.

# Text Similarity

> Quantifies the overall textual resemblance between a generated summary and a reference summary by using character-level fuzzy matching. This metric accounts for minor variations in wording or structure while still capturing how similar the two texts are at a surface level.

The Text Similarity metric is one of the [Deterministic Metric](/concepts/metric) options provided by Galtea. It evaluates how closely a generated text resembles a reference by using fuzzy string matching techniques. This approach is especially valuable in scenarios where flexible wording, paraphrasing, or non-exact phrasing is acceptable, but a general sense of alignment is still important.

## Evaluation Parameters

To compute the text\_similarity metric, the following parameters must be provided:

* **`actual_output`**: The generated text or summary from the model.
* **`expected_output`**: The reference or ground truth text to compare against.

## How Is It Calculated?

The metric uses a fuzzy string matching algorithm, which compares the characters in both strings and calculates how many edits (insertions, deletions, or substitutions) are needed to transform one into the other.

## Interpretation of Scores

* **≥ 0.8** – High textual similarity; close match.
* **0.5 – 0.8** – Moderate similarity; captures most content.
* **\< 0.5** – Low similarity; outputs may diverge significantly.

## Suggested Test Case Types

Use Text Similarity when evaluating:

* **General text matching** where loose phrasing is acceptable.
* **Heuristic checks** for paraphrasing or alternative wording.
* **Lightweight similarity evaluations** without requiring semantic understanding.