# Text Summarization

{% hint style="info" %}
Exclusive to enterprise customers. [Contact us](https://calendly.com/nirmalya-raga/30min?month=2025-09) to activate this feature.
{% endhint %}

RagaAI provides several metrics for evaluating text summarization tasks, divided broadly into metrics based on N-gram overlap suited for extractive tasks (e.g, ROUGE, METEOR, BLEU) vs those using embeddings and LLM-as-a-judge suited for abstractive tasks (e.g, G-Eval, BERTScore, etc.). Here is a list of available metrics:

{% content-ref url="/pages/RpWMM3V6psL182RSwl84" %}
[Broken mention](broken://pages/RpWMM3V6psL182RSwl84)
{% endcontent-ref %}

{% content-ref url="/pages/yFh067y7q0WL0etqbDFV" %}
[Broken mention](broken://pages/yFh067y7q0WL0etqbDFV)
{% endcontent-ref %}

{% content-ref url="/pages/HO6h3UBaSULC8kUnBm6k" %}
[Broken mention](broken://pages/HO6h3UBaSULC8kUnBm6k)
{% endcontent-ref %}

{% content-ref url="/pages/Ozx4ppSdJm7uQmYJOqHI" %}
[Broken mention](broken://pages/Ozx4ppSdJm7uQmYJOqHI)
{% endcontent-ref %}

{% content-ref url="/pages/CzohjlIk628nXIwuGWWz" %}
[Broken mention](broken://pages/CzohjlIk628nXIwuGWWz)
{% endcontent-ref %}

{% content-ref url="/pages/Dj4u2n0Z9iAamyYHZJA0" %}
[Broken mention](broken://pages/Dj4u2n0Z9iAamyYHZJA0)
{% endcontent-ref %}

Additionally, Catalyst offers certain Summarization metrics that do not require LLM-as-a-judge for computation, including:

{% content-ref url="/pages/w4uFBzojEwbjqUFhxbKg" %}
[Broken mention](broken://pages/w4uFBzojEwbjqUFhxbKg)
{% endcontent-ref %}

{% content-ref url="/pages/6hVvv0LXqKwj2WhnEvKa" %}
[Broken mention](broken://pages/6hVvv0LXqKwj2WhnEvKa)
{% endcontent-ref %}

{% content-ref url="/pages/zNS8MPqLsOoF0kJyovbx" %}
[Broken mention](broken://pages/zNS8MPqLsOoF0kJyovbx)
{% endcontent-ref %}

{% content-ref url="/pages/xD46tTWcbe6rn5OlBy59" %}
[Broken mention](broken://pages/xD46tTWcbe6rn5OlBy59)
{% endcontent-ref %}


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/text-summarization.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.