summeval
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<!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md --> <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">SummEvalSummarization.v2</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div> </div> News Article Summary Semantic Similarity Estimation. This version fixes a bug in the evaluation script that caused the main score to be computed incorrectly. | | | |---------------|---------------------------------------------| | Task category | t2t | | Domains | News, Written | | Reference | https://github.com/Yale-LILY/SummEval | ## How to evaluate on this task You can evaluate an embedding model on this dataset using the following code: ```python import mteb task = mteb.get_tasks(["SummEvalSummarization.v2"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) ``` <!-- Datasets want link to arxiv in readme to autolink dataset with paper --> To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). ## Citation If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb). ```bibtex @article{fabbri2020summeval, author = {Fabbri, Alexander R and Kry{\'s}ci{\'n}ski, Wojciech and McCann, Bryan and Xiong, Caiming and Socher, Richard and Radev, Dragomir}, journal = {arXiv preprint arXiv:2007.12626}, title = {SummEval: Re-evaluating Summarization Evaluation}, year = {2020}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } ``` # Dataset Statistics <details> <summary> Dataset Statistics</summary> The following code contains the descriptive statistics from the task. These can also be obtained using: ```python import mteb task = mteb.get_task("SummEvalSummarization.v2") desc_stats = task.metadata.descriptive_stats ``` ```json { "test": { "num_samples": 100, "number_of_characters": 212735, "min_text_length": 626, "avg_text_length": 2100.35, "max_text_length": 3153, "unique_texts": 100, "min_human_summaries_length": 11, "avg_human_summaries_length": 11.0, "max_human_summaries_length": 11, "unique_human_summaries": 1100, "min_machine_summaries_length": 16, "avg_machine_summaries_length": 16.0, "max_machine_summaries_length": 16, "unique_machine_summaries": 1548, "min_relevance": [ 1.0, 1.3333333333333333, 3.6666666666666665, 2.3333333333333335, 3.6666666666666665, 3.0, 4.333333333333333, 4.0, 2.6666666666666665, 4.0, 2.0, 4.666666666666667, 4.333333333333333, 1.0, 2.0, 1.0 ], "avg_relevance": 3.7770833333333336, "max_relevance": [ 5.0, 4.666666666666667, 4.333333333333333, 2.6666666666666665, 4.666666666666667, 4.666666666666667, 4.666666666666667, 4.333333333333333, 4.0, 4.333333333333333, 4.666666666666667, 4.666666666666667, 4.333333333333333, 2.3333333333333335, 4.666666666666667, 4.666666666666667 ] } } ``` </details> --- *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
<div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;"> <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">SummEvalSummarization.v2</h1> <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">隶属于 <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> 的数据集</div> <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">大规模文本嵌入基准（Massive Text Embedding Benchmark，MTEB）</div> </div> 新闻文章摘要语义相似度评估。本版本修复了评估脚本中导致主评分计算错误的漏洞。 | | | |---------------|---------------------------------------------| | 任务类别 | t2t | | 领域 | 新闻、书面文本 | | 参考来源 | https://github.com/Yale-LILY/SummEval | ## 本任务评估方法 你可通过以下代码在该数据集上评估嵌入模型： python import mteb task = mteb.get_tasks(["SummEvalSummarization.v2"]) evaluator = mteb.MTEB(task) model = mteb.get_model(YOUR_MODEL) evaluator.run(model) <!-- 数据集自述文件中需添加arXiv链接以自动关联数据集与对应论文 --> 若需了解如何在MTEB任务上运行模型，请参阅其[GitHub仓库](https://github.com/embeddings-benchmark/mteb)。 ## 引用声明 若使用本数据集，请同时引用该数据集与[MTEB](https://github.com/embeddings-benchmark/mteb)，因为本数据集作为[大规模多语言文本嵌入基准（Massive Multilingual Text Embedding Benchmark，MMTEB）贡献项](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb)的一部分经过了额外处理。 bibtex @article{fabbri2020summeval, author = {Fabbri, Alexander R and Kry\'s}ci\'nski, Wojciech and McCann, Bryan and Xiong, Caiming and Socher, Richard and Radev, Dragomir}, journal = {arXiv preprint arXiv:2007.12626}, title = {SummEval: Re-evaluating Summarization Evaluation}, year = {2020}, } @article{enevoldsen2025mmtebmassivemultilingualtext, title={MMTEB: Massive Multilingual Text Embedding Benchmark}, author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff}, publisher = {arXiv}, journal={arXiv preprint arXiv:2502.13595}, year={2025}, url={https://arxiv.org/abs/2502.13595}, doi = {10.48550/arXiv.2502.13595}, } @article{muennighoff2022mteb, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} url = {https://arxiv.org/abs/2210.07316}, doi = {10.48550/ARXIV.2210.07316}, } # 数据集统计信息 <details> <summary>数据集统计信息</summary> 以下为本任务的描述性统计数据，你也可通过以下代码获取： python import mteb task = mteb.get_task("SummEvalSummarization.v2") desc_stats = task.metadata.descriptive_stats json { "test": { "num_samples": 100, "number_of_characters": 212735, "min_text_length": 626, "avg_text_length": 2100.35, "max_text_length": 3153, "unique_texts": 100, "min_human_summaries_length": 11, "avg_human_summaries_length": 11.0, "max_human_summaries_length": 11, "unique_human_summaries": 1100, "min_machine_summaries_length": 16, "avg_machine_summaries_length": 16.0, "max_machine_summaries_length": 16, "unique_machine_summaries": 1548, "min_relevance": [ 1.0, 1.3333333333333333, 3.6666666666666665, 2.3333333333333335, 3.6666666666666665, 3.0, 4.333333333333333, 4.0, 2.6666666666666665, 4.0, 2.0, 4.666666666666667, 4.333333333333333, 1.0, 2.0, 1.0 ], "avg_relevance": 3.7770833333333336, "max_relevance": [ 5.0, 4.666666666666667, 4.333333333333333, 2.6666666666666665, 4.666666666666667, 4.666666666666667, 4.666666666666667, 4.333333333333333, 4.0, 4.333333333333333, 4.666666666666667, 4.666666666666667, 4.333333333333333, 2.3333333333333335, 4.666666666666667, 4.666666666666667 ] } } </details> --- *本数据集卡片由 [MTEB](https://github.com/embeddings-benchmark/mteb) 自动生成*
