data card.

README.md

@@ -1,174 +1,265 @@
 ---
-pretty_name: OrangeSum
 annotations_creators:
-- found
+- unknown
 language_creators:
-- found
+- unknown
 languages:
-- fr
-licenses:
 - unknown
+licenses:
+- other
 multilinguality:
-- monolingual
+- unknown
+pretty_name: OrangeSum
 size_categories:
-- 10K<n<100K
+- unknown
 source_datasets:
 - original
 task_categories:
-- conditional-text-generation
+- unknown
 task_ids:
-- summarization
-paperswithcode_id: orangesum
+- unknown
 ---
 
-# Dataset Card for OrangeSum
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
+# Dataset Card for GEM/OrangeSum
 
 ## Dataset Description
 
-- **Repository:** [OrangeSum repository](https://github.com/Tixierae/OrangeSum)
-- **Paper:** [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321)	
-- **Point of Contact:** [Antoine J.-P. Tixier](Antoine.Tixier-1@colorado.edu)
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/Tixierae/OrangeSum
+- **Paper:** https://aclanthology.org/2021.emnlp-main.740
+- **Leaderboard:** N/A
+- **Point of Contact:** [Needs More Information]
 
-### Dataset Summary
+### Link to Main Data Card
 
-The OrangeSum dataset was inspired by the XSum dataset. It was created by scraping the "Orange Actu" website: https://actu.orange.fr/. Orange S.A. is a large French multinational telecommunications corporation, with 266M customers worldwide. Scraped pages cover almost a decade from Feb 2011 to Sep 2020. They belong to five main categories: France, world, politics, automotive, and society. The society category is itself divided into 8 subcategories: health, environment, people, culture, media, high-tech, unsual ("insolite" in French), and miscellaneous.
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/OrangeSum).
 
-Each article featured a single-sentence title as well as a very brief abstract, both professionally written by the author of the article. These two fields were extracted from each page, thus creating two summarization tasks: OrangeSum Title and OrangeSum Abstract.
+### Dataset Summary 
 
-### Supported Tasks and Leaderboards
+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. 
 
-**Tasks:** OrangeSum Title and OrangeSum Abstract.
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/OrangeSum')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/OrangeSum).
 
-To this day, there is no Leaderboard for this dataset.
+#### paper
+[ACL Anthology](https://aclanthology.org/2021.emnlp-main.740)
 
-### Languages
+## Dataset Overview
 
-The text in the dataset is in French.
+### Where to find the Data and its Documentation
 
-## Dataset Structure
+#### Download
 
-### Data Instances
+<!-- info: What is the link to where the original dataset is hosted? -->
+<!-- scope: telescope -->
+[Github](https://github.com/Tixierae/OrangeSum)
 
-A data instance consists of a news article  and a summary. The summary can be a short abstract or a title depending on the configuration.
+#### Paper
 
-Example:
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+[ACL Anthology](https://aclanthology.org/2021.emnlp-main.740)
 
-**Document:** Le temps sera pluvieux sur huit départements de la France ces prochaines heures : outre les trois départements bretons placés en vigilance orange jeudi matin, cinq autres départements du sud du Massif Central ont été à leur tour placés en alerte orange pluie et inondation. Il s'agit de l'Aveyron, du Cantal, du Gard, de la Lozère, et de la Haute-Loire. Sur l'ensemble de l'épisode, les cumuls de pluies attendus en Bretagne sont compris entre 40 et 60 mm en 24 heures et peuvent atteindre localement les 70 mm en 24 heures.Par la suite, la dégradation qui va se mettre en place cette nuit sur le Languedoc et le sud du Massif Central va donner sur l'Aveyron une première salve intense de pluie. Des cumuls entre 70 et 100 mm voir 120 mm localement sont attendus sur une durée de 24 heures. Sur le relief des Cévennes on attend de 150 à 200 mm, voire 250 mm très ponctuellement sur l'ouest du Gard et l'est de la Lozère. Cet épisode va s'estomper dans la soirée avec le décalage des orages vers les régions plus au nord. Un aspect orageux se mêlera à ces précipitations, avec de la grêle possible, des rafales de vent et une forte activité électrique.
+#### BibTex
 
-**Abstract:** Outre les trois départements bretons, cinq autres départements du centre de la France ont été placés en vigilance orange pluie-inondation.
+<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
+<!-- scope: microscope -->
+```
+@inproceedings{kamal-eddine-etal-2021-barthez,
+    title = "{BART}hez: a Skilled Pretrained {F}rench Sequence-to-Sequence Model",
+    author = "Kamal Eddine, Moussa  and
+      Tixier, Antoine  and
+      Vazirgiannis, Michalis",
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.740",
+    doi = "10.18653/v1/2021.emnlp-main.740",
+    pages = "9369--9390",
+    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.",
+}
+```
 
-**Title:** Pluie-inondations : 8 départements en alerte orange.
+#### Has a Leaderboard?
 
-### Data Fields
+<!-- info: Does the dataset have an active leaderboard? -->
+<!-- scope: telescope -->
+no
 
-`text`: the document to be summarized. \
-`summary`: the summary of the source document.
 
-### Data Splits
+### Languages and Intended Use
 
-The data is split into a training, validation and test in both configuration.
+#### Multilingual?
 
-|          | Tain   | Valid | Test |
-| -----    | ------ | ----- | ---- |
-| Abstract | 21400  |  1500 | 1500 |
-| Title    | 30658  |  1500 | 1500 |
+<!-- quick -->
+<!-- info: Is the dataset multilingual? -->
+<!-- scope: telescope -->
+no
 
-## Dataset Creation
+#### License
 
-### Curation Rationale
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+other: Other license
 
-The goal here was to create a French equivalent of the recently introduced [XSum](https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset) dataset. Unlike the historical summarization datasets, CNN, DailyMail, and NY Times, which favor extractive strategies, XSum, as well as OrangeSum require the models to display a high degree of abstractivity to perform well. The summaries in OrangeSum are not catchy headlines, but rather capture the gist of the articles.
 
-### Source Data
+### Credit
 
-#### Initial Data Collection and Normalization
 
-Each article features a single-sentence title as well as a very brief abstract. Extracting these two fields from each news article page, creates two summarization tasks: OrangeSum Title and OrangeSum Abstract. As a post-processing step, all empty articles and those whose summaries were shorter than 5 words were removed. For OrangeSum Abstract, the top 10% articles in terms of proportion of novel unigrams in the abstracts were removed, as it was observed that such abstracts tend to be introductions rather than real abstracts. This corresponded to a threshold of 57% novel unigrams. For both OrangeSum Title and OrangeSum Abstract, 1500 pairs for testing and 1500 for validation are set aside, and all the remaining ones are used for training.
 
-#### Who are the source language producers?
+### Dataset Structure
 
-The authors of the artiles.
 
-### Annotations
 
-#### Annotation process
 
-The smmaries are professionally written by the author of the articles.
+## Dataset in GEM
 
-#### Who are the annotators?
+### Rationale for Inclusion in GEM
 
-The authors of the artiles.
+#### Similar Datasets
 
-### Personal and Sensitive Information
+<!-- info: Do other datasets for the high level task exist? -->
+<!-- scope: telescope -->
+no
 
-[More Information Needed]
 
-## Considerations for Using the Data
+### GEM-Specific Curation
+
+#### Modificatied for GEM?
+
+<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
+<!-- scope: telescope -->
+no
+
+#### Additional Splits?
+
+<!-- info: Does GEM provide additional splits to the dataset? -->
+<!-- scope: telescope -->
+no
+
+
+### Getting Started with the Task
+
+#### Pointers to Resources
+
+<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
+<!-- scope: microscope -->
+Papers about abstractive summarization using seq2seq models:
+https://aclanthology.org/K16-1028/
+https://aclanthology.org/P17-1099/
+https://aclanthology.org/2020.acl-main.703
+https://aclanthology.org/2021.emnlp-main.740/
+
+Papers about (pretrained) Transformers:
+https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
+https://aclanthology.org/N19-1423/
+https://aclanthology.org/2020.acl-main.703
+
+#### Technical Terms
+
+<!-- info: Technical terms used in this card and the dataset and their definitions -->
+<!-- scope: microscope -->
+No unique technical words in this data card.
+
+
+
+## Previous Results
+
+### Previous Results
 
-### Social Impact of Dataset
+#### Measured Model Abilities
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: telescope -->
+The ability of the model to generate human like titles and abstracts for given news articles.
+
+#### Metrics
+
+<!-- info: What metrics are typically used for this task? -->
+<!-- scope: periscope -->
+`ROUGE`, `BERT-Score`
+
+#### Proposed Evaluation
+
+<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
+<!-- scope: microscope -->
+Automatic Evaluation: Rouge-1, Rouge-2, RougeL and BERTScore were used.
+
+Human 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?).
+
+#### Previous results available?
+
+<!-- info: Are previous results available? -->
+<!-- scope: telescope -->
+no
+
+
+
+## Broader Social Context
+
+### Previous Work on the Social Impact of the Dataset
+
+#### Usage of Models based on the Data
+
+<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
+<!-- scope: telescope -->
+no
+
+
+### Impact on Under-Served Communities
+
+#### Addresses needs of underserved Communities?
+
+<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
+<!-- scope: telescope -->
+no
 
-[More Information Needed]
 
 ### Discussion of Biases
 
-[More Information Needed]
+#### Any Documented Social Biases?
 
-### Other Known Limitations
+<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
+<!-- scope: telescope -->
+no
 
-[More Information Needed]
+#### Are the Language Producers Representative of the Language?
 
-## Additional Information
+<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
+<!-- scope: periscope -->
+The dataset contains news articles written by professional authors.
 
-### Dataset Curators
 
-The dataset was initially created by Antoine J.-P. Tixier.
 
-### Licensing Information
+## Considerations for Using the Data
 
-[More Information Needed]
+### PII Risks and Liability
 
-### Citation Information
 
-```
-@inproceedings{kamal-eddine-etal-2021-barthez,
-    title = "{BART}hez: a Skilled Pretrained {F}rench Sequence-to-Sequence Model",
-    author = "Kamal Eddine, Moussa  and
-      Tixier, Antoine  and
-      Vazirgiannis, Michalis",
-    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
-    month = nov,
-    year = "2021",
-    address = "Online and Punta Cana, Dominican Republic",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.emnlp-main.740",
-    pages = "9369--9390",
-}
-```
 
-### Contributions
+### Licenses
+
+#### Copyright Restrictions on the Dataset
+
+<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
+<!-- scope: periscope -->
+`open license - commercial use allowed`
+
+#### Copyright Restrictions on the Language Data
+
+<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
+<!-- scope: periscope -->
+`open license - commercial use allowed`
+
+
+### Known Technical Limitations
+
+
 
-Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset.