{
 "body": "\nPart of the [MTEB](mteb.md) family.\n\n## What it measures\n\nmteb_summarization embeds machine-generated and human-written summaries of the same source\narticles, scores each machine summary by its embedding's similarity to the human references,\nand checks whether that similarity score ranks summaries the same way human quality ratings do\n(Spearman correlation). Unlike the other task types, the original MTEB task set covers this\nwith a single English dataset (SummEval), making it the thinnest-evidence task type in the\nsuite.\n\n## Reading the numbers\n\nBecause it rests on one dataset, mteb_summarization is the least statistically robust MTEB\nsubset \u2014 a model's score here should carry much less weight in a purchasing decision than its\nretrieval or classification score, and small differences between models are unlikely to be\nmeaningful. It measures whether an embedding model is useful for scoring or filtering\nmachine-generated summaries, not whether the model itself can summarize text, since embedding\nmodels do not generate text.\n",
 "build": {
  "built_at": "2026-09-09T16:56:50+00:00",
  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
  "canonical_id": "mteb_summarization",
  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
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 ],
 "page": {
  "aliases": [],
  "category": "embedding",
  "contamination": {
   "note": "SummEval and its source articles are public; being a single dataset also means any leakage has an outsized effect on the score (see caveat in Reading the numbers).",
   "risk": "medium"
  },
  "dataset": {
   "languages": [],
   "license": "Varies by dataset.",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 1,
   "size_note": "A single English dataset (SummEval) in the original MTEB task set \u2014 the thinnest-evidence task type in the suite.",
   "splits": "",
   "url": "https://huggingface.co/spaces/mteb/leaderboard"
  },
  "freshness": {
   "researched": "2026-09-07",
   "researched_by": "sonnet-5 agent, batch 1, slice D",
   "reviewed": "",
   "reviewed_by": ""
  },
  "harness": {
   "bigbench": "",
   "helm": "",
   "inspect_evals": "",
   "lm_eval": "",
   "opencompass": "",
   "other": "Run the Summarization task group in the mteb Python package (pip install mteb; mteb run -t <summarization-task-name>)."
  },
  "id": "mteb_summarization",
  "last_updated": "",
  "leaderboard_url": "https://huggingface.co/spaces/mteb/leaderboard",
  "lineage": {
   "family": "mteb",
   "predecessor": "",
   "successors": [],
   "variants": []
  },
  "measures": "Machine-generated and human-written summaries of the same source articles are embedded, and each machine summary is scored by its embedding's similarity to the human references. That similarity score is then checked against human quality ratings.\n",
  "metric": {
   "baseline_note": "",
   "direction": "higher_is_better",
   "human_baseline": null,
   "max_score": 100,
   "name": "Spearman correlation (cosine similarity)",
   "random_baseline": null,
   "unit": "%"
  },
  "name": "MTEB Summarization",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2210.07316",
   "title": "MTEB: Massive Text Embedding Benchmark",
   "url": "https://aclanthology.org/2023.eacl-main.148/",
   "year": 2023
  },
  "publisher": {
   "authors": [
    "Niklas Muennighoff",
    "Nouamane Tazi",
    "Lo\u00efc Magne",
    "Nils Reimers"
   ],
   "org": "Hugging Face and Cohere (original authors); maintained today by the open embeddings-benchmark community",
   "url": "https://github.com/embeddings-benchmark"
  },
  "released": "2022-10",
  "repo_url": "https://github.com/embeddings-benchmark/mteb",
  "saturation": {
   "as_of": "",
   "note": "Not established from a source read for this page. Resting on a single dataset, this task type is inherently noisy regardless of saturation status.",
   "status": "unknown",
   "top_score": null
  },
  "sources": [
   {
    "accessed": "2026-09-07",
    "title": "MTEB: Massive Text Embedding Benchmark (arXiv preprint)",
    "url": "https://arxiv.org/abs/2210.07316"
   },
   {
    "accessed": "2026-09-07",
    "title": "embeddings-benchmark/mteb GitHub repository",
    "url": "https://github.com/embeddings-benchmark/mteb"
   },
   {
    "accessed": "2026-09-07",
    "title": "MTEB Leaderboard (Hugging Face Space)",
    "url": "https://huggingface.co/spaces/mteb/leaderboard"
   }
  ],
  "status": "active",
  "subcategory": "summarization",
  "summary": "Scores whether embedding similarity to human-written summaries predicts human quality ratings of machine summaries, on a single English dataset.",
  "tags": [
   "embedding",
   "summarization"
  ],
  "task_format": "Embed machine and human summaries; score each machine summary by embedding similarity to human references; correlate (Spearman) against human quality ratings."
 }
}