# Summarization

**Objective**: The test determines if LLM generates factually correct summaries with necessary details.

**Required Parameters**: Prompt, Response

**Interpretation**: A higher score signifies better summary quality.

```python
# Here response(summary) is factually correct and contains details, so the test is passed.
evaluator.add_test(
    test_names=["summarisation_test"],
    data={
        "prompt": ["Summarize: In the realm of sports, athleticism intertwines with passion and competition. From the roar of stadiums to the grit of training grounds, athletes inspire with their feats, uniting diverse communities worldwide."],
        "response": ["Sports epitomize human passion and unity, showcasing athleticism's prowess and communal bonds across global arenas."]
    },
    arguments={"model": "gpt-4", "threshold": 0.5},
).run()

evaluator.print_results()
```

```python
# Here response(summary) is not factually correct, so the test is failed.
evaluator.add_test(
    test_names=["summarisation_test"],
    data={
        "prompt": ["Summarize: World War I was triggered by a complex web of political, economic, and social factors. The assassination of Archduke Franz Ferdinand, militarism, alliances, and imperialistic ambitions were political causes. Economically, competition and colonial rivalries played a role. Social tensions also contributed. Consequences included the Treaty of Versailles, redrawing of borders, and the League of Nations."],
        "response": ["World War I was triggered because of environmental factors."]
    },
    arguments={"model": "gpt-4", "threshold": 0.5},
).run()

evaluator.print_results()
```


---

# 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/additional-metrics/evaluation/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.