xsum.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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
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# Lint as: python3
"""XSum dataset."""

from __future__ import absolute_import, division, print_function

import json
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 three features:
  - document: Input news article.
  - summary: One sentence summary of the article.
  - id: BBC ID of the article.

"""

# From https://github.com/EdinburghNLP/XSum/issues/12
_URL_DATA = "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz"
_URL_SPLITS = (
    "https://raw.githubusercontent.com/EdinburghNLP/XSum/master/XSum-Dataset/XSum-TRAINING-DEV-TEST-SPLIT-90-5-5.json"
)

_DOCUMENT = "document"
_SUMMARY = "summary"
_ID = "id"

_REMOVE_LINES = set(
    [
        "Share this with\n",
        "Email\n",
        "Facebook\n",
        "Messenger\n",
        "Twitter\n",
        "Pinterest\n",
        "WhatsApp\n",
        "Linkedin\n",
        "LinkedIn\n",
        "Copy this link\n",
        "These are external links and will open in a new window\n",
    ]
)


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

    # Version 1.2.0 expands coverage, includes ids, and removes web contents.
    VERSION = datasets.Version("1.2.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    _DOCUMENT: datasets.Value("string"),
                    _SUMMARY: datasets.Value("string"),
                    _ID: 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."""

        files_to_download = {"data": _URL_DATA, "splits": _URL_SPLITS}
        downloaded_files = dl_manager.download_and_extract(files_to_download)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "split_path": downloaded_files["splits"],
                    "split_name": "train",
                    "data_dir": os.path.join(downloaded_files["data"], "bbc-summary-data"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "split_path": downloaded_files["splits"],
                    "split_name": "validation",
                    "data_dir": os.path.join(downloaded_files["data"], "bbc-summary-data"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "split_path": downloaded_files["splits"],
                    "split_name": "test",
                    "data_dir": os.path.join(downloaded_files["data"], "bbc-summary-data"),
                },
            ),
        ]

    def _generate_examples(self, split_path, split_name, data_dir):
        """Yields examples."""

        with open(split_path, "r", encoding="utf-8") as f:
            split_ids = json.load(f)

        for i in split_ids[split_name]:
            with open(os.path.join(data_dir, i + ".summary"), "r", encoding="utf-8") as f:
                text = "".join([line for line in f.readlines() if line not in _REMOVE_LINES and line.strip()])
                # Each file follows below format:
                # [SN]URL[SN]
                # http://somelink
                #
                # [SN]TITLE[SN]
                # some intro
                #
                # [SN]FIRST-SENTENCE[SN]
                # some intro
                #
                # [SN]RESTBODY[SN]
                # text line.
                # another text line.
                # "another text line."

                # According to the following issue, FIRST-SENTENCE
                # is the reference summary and TITLE is unused:
                # https://github.com/EdinburghNLP/XSum/issues/22
                segs = text.split("[SN]")
                yield i, {_DOCUMENT: segs[8].strip(), _SUMMARY: segs[6].strip(), _ID: i}