📄 De¶

We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages – namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
We report cross-lingual comparative analyses based on state-of-the-art systems.
These highlight existing biases which motivate the use of a multi-lingual dataset.

Tags: annotations_creators:found, language:['de', 'es', 'fr', 'ru', 'tr'], language_creators:found, license:other, multilinguality:multilingual, region:us, size_categories:['100K<n<1M', '10K<n<100K'], source_datasets:['extended|cnn_dailymail', 'original'], task_categories:['summarization', 'translation', 'text-classification'], task_ids:['news-articles-summarization', 'multi-class-classification', 'multi-label-classification', 'topic-classification']

Note

ID: cards.mlsum.de | Type: TaskCard

{
    "__description__": "We present MLSUM, the first large-scale MultiLingual SUMmarization dataset.\nObtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.\nTogether with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.\nWe report cross-lingual comparative analyses based on state-of-the-art systems.\nThese highlight existing biases which motivate the use of a multi-lingual dataset.",
    "__tags__": {
        "annotations_creators": "found",
        "language": [
            "de",
            "es",
            "fr",
            "ru",
            "tr"
        ],
        "language_creators": "found",
        "license": "other",
        "multilinguality": "multilingual",
        "region": "us",
        "size_categories": [
            "100K<n<1M",
            "10K<n<100K"
        ],
        "source_datasets": [
            "extended|cnn_dailymail",
            "original"
        ],
        "task_categories": [
            "summarization",
            "translation",
            "text-classification"
        ],
        "task_ids": [
            "news-articles-summarization",
            "multi-class-classification",
            "multi-label-classification",
            "topic-classification"
        ]
    },
    "__type__": "task_card",
    "loader": {
        "__type__": "load_hf",
        "name": "de",
        "path": "mlsum"
    },
    "preprocess_steps": [
        {
            "__type__": "rename_fields",
            "field_to_field": {
                "text": "document"
            }
        }
    ],
    "task": "tasks.summarization.abstractive",
    "templates": "templates.summarization.abstractive.all"
}

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into a model-input, and specifies the metrics for evaluation of model-output.

Attributes:
loader: specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps: list of unitxt operators to process the data source into a model-input.

task: specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates: format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

Explanation about LoadHF¶

Loads datasets from the Huggingface Hub.

It supports loading with or without streaming, and can filter datasets upon loading.
Args:
path: The path or identifier of the dataset on the Huggingface Hub. name: An optional dataset name. data_dir: Optional directory to store downloaded data. split: Optional specification of which split to load. data_files: Optional specification of particular data files to load. streaming: Bool indicating if streaming should be used. filtering_lambda: A lambda function for filtering the data after loading. num_proc: Optional integer to specify the number of processes to use for parallel dataset loading.

Example:
Loading glue’s mrpc dataset
load_hf = LoadHF(path='glue', name='mrpc')

Explanation about RenameFields¶

Renames fields.

Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.

Examples:
RenameFields(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]

RenameFields(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]

RenameFields(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]

RenameFields(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]

References: templates.summarization.abstractive.all, tasks.summarization.abstractive