{
  "gem": {
    "rationale": {
      "sole-task-dataset": "no",
      "sole-language-task-dataset": "N/A",
      "distinction-description": "N/A"
    },
    "curation": {
      "has-additional-curation": "no",
      "modification-types": [],
      "modification-description": "N/A",
      "has-additional-splits": "no",
      "additional-splits-description": "N/A",
      "additional-splits-capacicites": "N/A"
    },
    "starting": {
      "research-pointers": "Papers about abstractive summarization using seq2seq models:\n\n- [Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond](https://aclanthology.org/K16-1028/)\n- [Get To The Point: Summarization with Pointer-Generator Networks](https://aclanthology.org/P17-1099/)\n- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://aclanthology.org/2020.acl-main.703)\n- [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://aclanthology.org/2021.emnlp-main.740/)\n\nPapers about (pretrained) Transformers:\n\n- [Attention is All you Need](https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html)\n- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)",
      "technical-terms": "No unique technical words in this data card."
    }
  },
  "results": {
    "results": {
      "other-metrics-definitions": "N/A",
      "has-previous-results": "no",
      "current-evaluation": "N/A",
      "previous-results": "N/A",
      "metrics": [
        "ROUGE",
        "BERT-Score"
      ],
      "model-abilities": "The ability of the model to generate human like titles and abstracts for given news articles.",
      "original-evaluation": "Automatic Evaluation: Rouge-1, Rouge-2, RougeL and BERTScore were used.\n\nHuman evalutaion:  a human evaluation study was conducted with 11 French native speakers. The evaluators were PhD students from the computer science department of the university of the authors, working in NLP and other fields of AI. They volunteered after receiving an email announcement. the best-Worst Scaling (Louviere et al.,2015) was used. Two summaries from two different systems, along with their input document, were presented to a human annotator who had to decide which one was better.  The evaluators were asked to base their judgments on accuracy (does the summary contain accurate facts?), informativeness (is important in-formation captured?) and fluency (is the summary written in well-formed French?)."
    }
  },
  "considerations": {
    "pii": {
      "risks-description": "N/A"
    },
    "licenses": {
      "dataset-restrictions-other": "N/A",
      "data-copyright-other": "N/A",
      "dataset-restrictions": [
        "open license - commercial use allowed"
      ],
      "data-copyright": [
        "open license - commercial use allowed"
      ]
    },
    "limitations": {}
  },
  "context": {
    "previous": {
      "is-deployed": "no",
      "described-risks": "N/A",
      "changes-from-observation": "N/A"
    },
    "underserved": {
      "helps-underserved": "no",
      "underserved-description": "N/A"
    },
    "biases": {
      "has-biases": "no",
      "bias-analyses": "N/A",
      "speaker-distibution": "The dataset contains news articles written by professional authors."
    }
  },
  "overview": {
    "what": {
      "dataset": "OrangeSum is a French summarization dataset inspired by XSum. It features two subtasks: abstract generation and title generation. The data was sourced from \"Orange Actu\" articles between 2011 and 2020. "
    },
    "where": {
      "data-url": "[Github](https://github.com/Tixierae/OrangeSum)",
      "paper-url": "[ACL Anthology](https://aclanthology.org/2021.emnlp-main.740)",
      "paper-bibtext": "```\n@inproceedings{kamal-eddine-etal-2021-barthez,\n    title = \"{BART}hez: a Skilled Pretrained {F}rench Sequence-to-Sequence Model\",\n    author = \"Kamal Eddine, Moussa  and\n      Tixier, Antoine  and\n      Vazirgiannis, Michalis\",\n    booktitle = \"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2021\",\n    address = \"Online and Punta Cana, Dominican Republic\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.emnlp-main.740\",\n    doi = \"10.18653/v1/2021.emnlp-main.740\",\n    pages = \"9369--9390\",\n    abstract = \"Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However, most of the available models and research have been conducted for English. In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART, BARThez is particularly well-suited for generative tasks. We evaluate BARThez on five discriminative tasks from the FLUE benchmark and two generative tasks from a novel summarization dataset, OrangeSum, that we created for this research. We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. We also continue the pretraining of a multilingual BART on BARThez{'} corpus, and show our resulting model, mBARThez, to significantly boost BARThez{'} generative performance.\",\n}\n```",
      "has-leaderboard": "no",
      "leaderboard-url": "N/A",
      "leaderboard-description": "N/A"
    },
    "languages": {
      "is-multilingual": "no",
      "license": "other: Other license",
      "task": "Summarization",
      "task-other": "N/A",
      "language-names": [
        "French"
      ]
    },
    "credit": {},
    "structure": {}
  }
}