xsum.py

# coding=utf-8
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""XSum dataset."""

from __future__ import absolute_import, division, print_function

import os

import datasets


_CITATION = """
@article{Narayan2018DontGM,
  title={Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization},
  author={Shashi Narayan and Shay B. Cohen and Mirella Lapata},
  journal={ArXiv},
  year={2018},
  volume={abs/1808.08745}
}
"""

_DESCRIPTION = """
Extreme Summarization (XSum) Dataset.

There are two features:
  - document: Input news article.
  - summary: One sentence summary of the article.

"""


_URL = "https://s3.amazonaws.com/datasets.huggingface.co/summarization/xsum.tar.gz"

_DOCUMENT = "document"
_SUMMARY = "summary"


class Xsum(datasets.GeneratorBasedBuilder):
    """Extreme Summarization (XSum) Dataset."""

    # Version 1.1.0 removes web contents.
    VERSION = datasets.Version("1.1.0")
    SUPPORTED_VERSIONS = [datasets.Version("1.0.0", "Dataset without cleaning.")]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    _DOCUMENT: datasets.Value("string"),
                    _SUMMARY: datasets.Value("string"),
                }
            ),
            supervised_keys=(_DOCUMENT, _SUMMARY),
            homepage="https://github.com/EdinburghNLP/XSum/tree/master/XSum-Dataset",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        dl_path = dl_manager.download_and_extract(_URL)

        dl_path = os.path.join(dl_path, "xsum")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "source": os.path.join(dl_path, "train.source"),
                    "target": os.path.join(dl_path, "train.target"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "source": os.path.join(dl_path, "val.source"),
                    "target": os.path.join(dl_path, "val.target"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "source": os.path.join(dl_path, "test.source"),
                    "target": os.path.join(dl_path, "test.target"),
                },
            ),
        ]

    def _generate_examples(self, source, target):
        """Yields examples."""
        with open(source, encoding="utf-8") as f1:
            source = f1.readlines()
        with open(target, encoding="utf-8") as f2:
            target = f2.readlines()
        assert len(source) == len(target)
        for i in range(len(target)):
            yield i, {_DOCUMENT: source[i], _SUMMARY: target[i]}