PSC

An MTEB dataset

Massive Text Embedding Benchmark

Polish Summaries Corpus

Task category	t2t
Domains	News, Written
Reference	http://www.lrec-conf.org/proceedings/lrec2014/pdf/1211_Paper.pdf

How to evaluate on this task

You can evaluate an embedding model on this dataset using the following code:

import mteb

task = mteb.get_tasks(["PSC"])
evaluator = mteb.MTEB(task)

model = mteb.get_model(YOUR_MODEL)
evaluator.run(model)

To learn more about how to run models on mteb task check out the GitHub repitory.

Citation

If you use this dataset, please cite the dataset as well as mteb, as this dataset likely includes additional processing as a part of the MMTEB Contribution.

@inproceedings{ogrodniczuk-kopec-2014-polish,
  abstract = {This article presents the Polish Summaries Corpus, a new resource created to support the development and evaluation of the tools for automated single-document summarization of Polish. The Corpus contains a large number of manual summaries of news articles, with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard. There are several summarizers developed specifically for Polish language, but their in-depth evaluation and comparison was impossible without a large, manually created corpus. We present in detail the process of text selection, annotation process and the contents of the corpus, which includes both abstract free-word summaries, as well as extraction-based summaries created by selecting text spans from the original document. Finally, we describe how that resource could be used not only for the evaluation of the existing summarization tools, but also for studies on the human summarization process in Polish language.},
  address = {Reykjavik, Iceland},
  author = {Ogrodniczuk, Maciej  and
Kope{\'c}, Mateusz},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)},
  editor = {Calzolari, Nicoletta  and
Choukri, Khalid  and
Declerck, Thierry  and
Loftsson, Hrafn  and
Maegaard, Bente  and
Mariani, Joseph  and
Moreno, Asuncion  and
Odijk, Jan  and
Piperidis, Stelios},
  month = may,
  pages = {3712--3715},
  publisher = {European Language Resources Association (ELRA)},
  title = {The {P}olish Summaries Corpus},
  url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/1211_Paper.pdf},
  year = {2014},
}


@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

Dataset Statistics

The following code contains the descriptive statistics from the task. These can also be obtained using:

import mteb

task = mteb.get_task("PSC")

desc_stats = task.metadata.descriptive_stats

{
    "test": {
        "num_samples": 1078,
        "number_of_characters": 1206570,
        "unique_pairs": 1074,
        "min_sentence1_length": 314,
        "avg_sentence1_length": 549.2820037105752,
        "max_sentence1_length": 1445,
        "unique_sentence1": 507,
        "min_sentence2_length": 293,
        "avg_sentence2_length": 569.9851576994434,
        "max_sentence2_length": 1534,
        "unique_sentence2": 406,
        "unique_labels": 2,
        "labels": {
            "0": {
                "count": 750
            },
            "1": {
                "count": 328
            }
        }
    }
}

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This dataset card was automatically generated using MTEB