Datasets:

EdinburghNLP
/

xsum

	# 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
	# limitations under the License.

	# Lint as: python3
	"""XSum dataset."""


	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 = "data/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(files_to_download)

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

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

	with open(split_path, "r", encoding="utf-8") as f:
	split_ids = json.load(f)
	split_ids = {k: set(v) for k, v in split_ids.items()}

	for path, f in files:
	if not split_ids[split_name]:
	break
	elif path.startswith(data_dir) and path.endswith(".summary"):
	i = os.path.basename(path).split(".")[0]
	if i in split_ids[split_name]:
	split_ids[split_name].remove(i)
	text = "".join(
	[
	line.decode("utf-8")
	for line in f.readlines()
	if line.decode("utf-8") 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}