# Text Summarization

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Exclusive to enterprise customers. [Contact us](https://calendly.com/nirmalya-raga/30min?month=2025-09) to activate this feature.
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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:

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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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Additionally, Catalyst offers certain Summarization metrics that do not require LLM-as-a-judge for computation, including:

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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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[Broken link](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/broken-reference)
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