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index bd22570f3bfab06965e86ab3fc867e5ba5b4959b..9b82fd1add505a883a7a9d4891286aaea0b68e52 100644
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 *.ogg filter=lfs diff=lfs merge=lfs -text
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diff --git a/README.md b/README.md
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..c3c9e257df3f5c93929194179ae477fb8c7e6fa7 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,405 @@
+---
+annotations_creators:
+- found
+language_creators:
+- unknown
+language:
+- de
+- en
+- fr
+- cs
+license:
+- cc-by-sa-4.0
+multilinguality:
+- unknown
+pretty_name: xwikis
+size_categories:
+- unknown
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids:
+- unknown
+---
+
+# Dataset Card for GEM/xwikis
+
+## Dataset Description
+
+- **Homepage:** https://github.com/lauhaide/clads
+- **Repository:** [Needs More Information]
+- **Paper:** https://arxiv.org/abs/2202.09583
+- **Leaderboard:** N/A
+- **Point of Contact:** Laura Perez-Beltrachini
+
+### Link to Main Data Card
+
+You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/xwikis).
+
+### Dataset Summary 
+
+The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation. 
+
+You can load the dataset via:
+```
+import datasets
+data = datasets.load_dataset('GEM/xwikis')
+```
+The data loader can be found [here](https://huggingface.co/datasets/GEM/xwikis).
+
+#### website
+[Github](https://github.com/lauhaide/clads)
+
+#### paper
+https://arxiv.org/abs/2202.09583
+
+#### authors
+Laura Perez-Beltrachini (University of Edinburgh)
+
+## Dataset Overview
+
+### Where to find the Data and its Documentation
+
+#### Webpage
+
+<!-- info: What is the webpage for the dataset (if it exists)? -->
+<!-- scope: telescope -->
+[Github](https://github.com/lauhaide/clads)
+
+#### Paper
+
+<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
+<!-- scope: telescope -->
+https://arxiv.org/abs/2202.09583
+
+#### BibTex
+
+<!-- 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{clads-emnlp,
+  author =      "Laura Perez-Beltrachini and Mirella Lapata",
+  title =       "Models and Datasets for Cross-Lingual Summarisation",
+  booktitle =   "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ",
+  year =        "2021",
+  address =     "Punta Cana, Dominican Republic",
+}
+```
+
+#### Contact Name
+
+<!-- quick -->
+<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+Laura Perez-Beltrachini
+
+#### Contact Email
+
+<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
+<!-- scope: periscope -->
+lperez@ed.ac.uk
+
+#### Has a Leaderboard?
+
+<!-- info: Does the dataset have an active leaderboard? -->
+<!-- scope: telescope -->
+no
+
+
+### Languages and Intended Use
+
+#### Multilingual?
+
+<!-- quick -->
+<!-- info: Is the dataset multilingual? -->
+<!-- scope: telescope -->
+yes
+
+#### Covered Languages
+
+<!-- quick -->
+<!-- info: What languages/dialects are covered in the dataset? -->
+<!-- scope: telescope -->
+`German`, `English`, `French`, `Czech`
+
+#### License
+
+<!-- quick -->
+<!-- info: What is the license of the dataset? -->
+<!-- scope: telescope -->
+cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
+
+#### Intended Use
+
+<!-- info: What is the intended use of the dataset? -->
+<!-- scope: microscope -->
+Cross-lingual and Multi-lingual single long input document abstractive summarisation.
+
+#### Primary Task
+
+<!-- info: What primary task does the dataset support? -->
+<!-- scope: telescope -->
+Summarization
+
+#### Communicative Goal
+
+<!-- quick -->
+<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
+<!-- scope: periscope -->
+Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity.
+
+
+### Credit
+
+#### Curation Organization Type(s)
+
+<!-- info: In what kind of organization did the dataset curation happen? -->
+<!-- scope: telescope -->
+`academic`
+
+#### Dataset Creators
+
+<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
+<!-- scope: microscope -->
+Laura Perez-Beltrachini (University of Edinburgh)
+
+#### Who added the Dataset to GEM?
+
+<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
+<!-- scope: microscope -->
+Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh)
+
+
+### Dataset Structure
+
+#### Data Splits
+
+<!-- info: Describe and name the splits in the dataset if there are more than one. -->
+<!-- scope: periscope -->
+For each language pair and direction there exists a train/valid/test split. 
+The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de).
+Train/valid are randomly split.
+
+
+
+## Dataset in GEM
+
+### Rationale for Inclusion in GEM
+
+#### Similar Datasets
+
+<!-- info: Do other datasets for the high level task exist? -->
+<!-- scope: telescope -->
+no
+
+
+### 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
+
+
+
+
+## Previous Results
+
+### Previous Results
+
+#### Measured Model Abilities
+
+<!-- info: What aspect of model ability can be measured with this dataset? -->
+<!-- scope: telescope -->
+- identification of entity salient information
+- translation
+- multi-linguality
+- cross-lingual transfer, zero-shot, few-shot
+
+#### Metrics
+
+<!-- info: What metrics are typically used for this task? -->
+<!-- scope: periscope -->
+`ROUGE`
+
+#### Previous results available?
+
+<!-- info: Are previous results available? -->
+<!-- scope: telescope -->
+yes
+
+#### Other Evaluation Approaches
+
+<!-- info: What evaluation approaches have others used? -->
+<!-- scope: periscope -->
+ROUGE-1/2/L
+
+
+
+## Dataset Curation
+
+### Original Curation
+
+#### Sourced from Different Sources
+
+<!-- info: Is the dataset aggregated from different data sources? -->
+<!-- scope: telescope -->
+no
+
+
+### Language Data
+
+#### How was Language Data Obtained?
+
+<!-- info: How was the language data obtained? -->
+<!-- scope: telescope -->
+`Found`
+
+#### Where was it found?
+
+<!-- info: If found, where from? -->
+<!-- scope: telescope -->
+`Single website`
+
+#### Data Validation
+
+<!-- info: Was the text validated by a different worker or a data curator? -->
+<!-- scope: telescope -->
+other
+
+#### Was Data Filtered?
+
+<!-- info: Were text instances selected or filtered? -->
+<!-- scope: telescope -->
+not filtered
+
+
+### Structured Annotations
+
+#### Additional Annotations?
+
+<!-- quick -->
+<!-- info: Does the dataset have additional annotations for each instance? -->
+<!-- scope: telescope -->
+found
+
+#### Annotation Service?
+
+<!-- info: Was an annotation service used? -->
+<!-- scope: telescope -->
+no
+
+#### Annotation Values
+
+<!-- info: Purpose and values for each annotation -->
+<!-- scope: microscope -->
+The input documents have section structure information.
+
+#### Any Quality Control?
+
+<!-- info: Quality control measures? -->
+<!-- scope: telescope -->
+validated by another rater
+
+#### Quality Control Details
+
+<!-- info: Describe the quality control measures that were taken. -->
+<!-- scope: microscope -->
+Bilingual annotators assessed the content overlap of source document and target summaries.
+
+
+### Consent
+
+#### Any Consent Policy?
+
+<!-- info: Was there a consent policy involved when gathering the data? -->
+<!-- scope: telescope -->
+no
+
+
+### Private Identifying Information (PII)
+
+#### Contains PII?
+
+<!-- quick -->
+<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
+<!-- scope: telescope -->
+no PII
+
+
+### Maintenance
+
+#### Any Maintenance Plan?
+
+<!-- info: Does the original dataset have a maintenance plan? -->
+<!-- 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
+
+
+### Discussion of Biases
+
+#### Any Documented Social Biases?
+
+<!-- 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
+
+
+
+## Considerations for Using the Data
+
+### PII Risks and Liability
+
+
+
+### 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 -->
+`public domain`
+
+#### 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 -->
+`public domain`
+
+
+### Known Technical Limitations
+
+
+
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diff --git a/xwikis.json b/xwikis.json
new file mode 100644
index 0000000000000000000000000000000000000000..b77541a5d68db074504d476368b75139938d1d12
--- /dev/null
+++ b/xwikis.json
@@ -0,0 +1,153 @@
+{
+  "overview": {
+    "what": {
+      "dataset": "The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation. "
+    },
+    "where": {
+      "has-leaderboard": "no",
+      "leaderboard-url": "N/A",
+      "leaderboard-description": "N/A",
+      "website": "[Github](https://github.com/lauhaide/clads)",
+      "paper-bibtext": "```\n@InProceedings{clads-emnlp,\n  author =      \"Laura Perez-Beltrachini and Mirella Lapata\",\n  title =       \"Models and Datasets for Cross-Lingual Summarisation\",\n  booktitle =   \"Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing \",\n  year =        \"2021\",\n  address =     \"Punta Cana, Dominican Republic\",\n}\n```",
+      "paper-url": "https://arxiv.org/abs/2202.09583",
+      "contact-name": "Laura Perez-Beltrachini",
+      "contact-email": "lperez@ed.ac.uk"
+    },
+    "languages": {
+      "is-multilingual": "yes",
+      "license": "cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International",
+      "task-other": "N/A",
+      "language-names": [
+        "German",
+        "English",
+        "French",
+        "Czech"
+      ],
+      "intended-use": "Cross-lingual and Multi-lingual single long input document abstractive summarisation.",
+      "license-other": "N/A",
+      "task": "Summarization",
+      "communicative": "Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity."
+    },
+    "credit": {
+      "organization-type": [
+        "academic"
+      ],
+      "creators": "Laura Perez-Beltrachini (University of Edinburgh)",
+      "gem-added-by": "Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh)"
+    },
+    "structure": {
+      "structure-splits": "For each language pair and direction there exists a train/valid/test split. \nThe test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de).\nTrain/valid are randomly split."
+    }
+  },
+  "curation": {
+    "original": {
+      "is-aggregated": "no",
+      "aggregated-sources": "N/A"
+    },
+    "language": {
+      "found": [
+        "Single website"
+      ],
+      "crowdsourced": [],
+      "created": "N/A",
+      "machine-generated": "N/A",
+      "validated": "other",
+      "is-filtered": "not filtered",
+      "filtered-criteria": "N/A",
+      "obtained": [
+        "Found"
+      ]
+    },
+    "annotations": {
+      "origin": "found",
+      "rater-number": "N/A",
+      "rater-qualifications": "N/A",
+      "rater-training-num": "N/A",
+      "rater-test-num": "N/A",
+      "rater-annotation-service-bool": "no",
+      "rater-annotation-service": [],
+      "values": "The input documents have section structure information.",
+      "quality-control": "validated by another rater",
+      "quality-control-details": "Bilingual annotators assessed the content overlap of source document and target summaries."
+    },
+    "consent": {
+      "has-consent": "no",
+      "consent-policy": "N/A",
+      "consent-other": "N/A"
+    },
+    "pii": {
+      "has-pii": "no PII",
+      "no-pii-justification": "N/A",
+      "is-pii-identified": "N/A",
+      "pii-identified-method": "N/A",
+      "is-pii-replaced": "N/A",
+      "pii-replaced-method": "N/A",
+      "pii-categories": []
+    },
+    "maintenance": {
+      "has-maintenance": "no",
+      "description": "N/A",
+      "contact": "N/A",
+      "contestation-mechanism": "N/A",
+      "contestation-link": "N/A",
+      "contestation-description": "N/A"
+    }
+  },
+  "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": {}
+  },
+  "results": {
+    "results": {
+      "other-metrics-definitions": "N/A",
+      "has-previous-results": "yes",
+      "current-evaluation": "ROUGE-1/2/L",
+      "previous-results": "N/A",
+      "model-abilities": "- identification of entity salient information\n- translation\n- multi-linguality\n- cross-lingual transfer, zero-shot, few-shot",
+      "metrics": [
+        "ROUGE"
+      ]
+    }
+  },
+  "considerations": {
+    "pii": {},
+    "licenses": {
+      "dataset-restrictions-other": "N/A",
+      "data-copyright-other": "N/A",
+      "dataset-restrictions": [
+        "public domain"
+      ],
+      "data-copyright": [
+        "public domain"
+      ]
+    },
+    "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"
+    }
+  }
+}
\ No newline at end of file
diff --git a/xwikis.py b/xwikis.py
new file mode 100644
index 0000000000000000000000000000000000000000..37603d9728acaa1d246413360ce9680fc2199519
--- /dev/null
+++ b/xwikis.py
@@ -0,0 +1,225 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{perez2021models,
+  title={Models and Datasets for Cross-Lingual Summarisation},
+  author={Perez-Beltrachini, Laura and Lapata, Mirella},
+  booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
+  pages={9408--9423},
+  year={2021}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+The XWikis Corpus (Perez-Beltrachini and Lapata, 2021) provides datasets with different language pairs and directions for cross-lingual abstractive document summarisation. This current version includes four languages: English, German, French, and Czech. The dataset is derived from Wikipedia. It is based on the observation that for a Wikipedia title, the lead section provides an overview conveying salient information, while the body provides detailed information. It thus assumes the body and lead paragraph as a document-summary pair. Furthermore, as a Wikipedia title can be associated with Wikipedia articles in various languages, 1) Wikipedia’s Interlanguage Links are used to find titles across languages and 2) given any two related Wikipedia titles, e.g., Huile d’Olive (French) and Olive Oil (English), the lead paragraph from one title is paired with the body of the other to derive cross-lingual pairs.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/4188"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "CC BY-SA 4.0"
+
+VERSION = datasets.Version("0.1.0")
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+
+LPAIRS = [
+    f"{x}-{y}" for x in ["en","fr","cs","de"] for y in ["en","fr","cs","de"] if x!=y
+]
+
+_URLs = {
+    "train": [ f"./train/{xy}.jsonl" for xy in LPAIRS],
+    #     "./train/en-fr.jsonl",
+    #     "./train/fr-en.jsonl",
+    #     "./train/en-de.jsonl",
+    #     "./train/de-en.jsonl",
+    #     "./train/en-cs.jsonl",
+    #     "./train/cs-en.jsonl",
+    #     "./train/fr-de.jsonl",
+    #     "./train/de-fr.jsonl",
+    #     "./train/fr-cs.jsonl",
+    #     "./train/cs-fr.jsonl",
+    #     "./train/de-cs.jsonl",
+    #     "./train/cs-de.jsonl",
+    # ],
+    "validation": [ f"./valid/{xy}.jsonl" for xy in LPAIRS],
+    # "validation": [
+    #     "./valid/en-fr.jsonl",
+    #     "./valid/fr-en.jsonl",
+    #     "./valid/en-de.jsonl",
+    #     "./valid/de-en.jsonl",
+    #     "./valid/en-cs.jsonl",
+    #     "./valid/cs-en.jsonl",
+    #     "./valid/fr-de.jsonl",
+    #     "./valid/de-fr.jsonl",
+    #     "./valid/fr-cs.jsonl",
+    #     "./valid/cs-fr.jsonl",
+    #     "./valid/de-cs.jsonl",
+    #     "./valid/cs-de.jsonl",
+    # ],
+    "test": [ f"./test/{xy}.jsonl" for xy in LPAIRS if "-en" in xy],
+    # "test": [
+    #     "./test/fr-en.jsonl",
+    #     "./test/de-en.jsonl",
+    #     "./test/cs-en.jsonl",
+    # ],
+}
+
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class XWikis(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [datasets.BuilderConfig(name=xy , version=VERSION, description=f"XWikis. Language pair: {xy}") for xy in LPAIRS]
+
+    DEFAULT_CONFIG_NAME = "fr-en"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            {
+                "gem_id": datasets.Value("string"),
+                "gem_parent_id": datasets.Value("string"),
+                "id": datasets.Value("string"),
+                "src_title": datasets.Value("string"),
+                "tgt_title": datasets.Value("string"),
+                "src_document": datasets.features.Sequence(
+                    {
+                        "title": datasets.Value("string"),
+                        "section_level": datasets.Value("string"),
+                        "content": datasets.Value("string"),
+                    }),
+                "src_summary": datasets.Value("string"),
+                "tgt_summary": datasets.Value("string")
+                
+                # These are the features of your dataset like images, labels ...
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        my_urls = {sp:f"./{sp[:5]}/{self.config.name}.jsonl" for sp in ["train","validation","test"] }
+        d_conf = dl_manager.download_and_extract(my_urls)
+        ### TODO
+        challenge_sets = []
+        # challenge_sets = [
+        #     ("challenge_test_abstractivity_%d" % (lvl), fname) \
+        #         for lvl,fname in enumerate(d_conf["cs_abs"])
+        # ] + [
+        #     ("challenge_test_topic_diversity_%d" % (lvl), fname) \
+        #         for lvl,fname in enumerate(d_conf["cs_abs"])
+        # ]
+        
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": d_conf["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": d_conf["validation"],
+                    "split": "validation"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": d_conf["test"],
+                    "split": "test",
+                },
+            ),
+        ] + [
+                datasets.SplitGenerator(
+                    name=challenge_split,
+                    gen_kwargs={
+                        "filepath": filename,
+                        "split": challenge_split,
+                    },
+                )
+                for challenge_split, filename in challenge_sets
+            ]
+
+    def _generate_examples(
+        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        with open(filepath, encoding="utf-8") as f:
+            for row in f:
+                data = json.loads(row)
+                id_ = data["id"]
+                # data["gem_parent_id"] = "GEM-wiki_cat_sum-%s-%d" % (split,data["id"]+1)
+                # data["gem_id"] = "GEM-wiki_cat_sum-%s-%d" % (split,data["id"]+1)
+                data["gem_parent_id"] = f"{self.config.name}-{split}-{id_}"
+                data["gem_id"] = f"{self.config.name}-{split}-{id_}"
+                yield id_,data