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# 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/data1.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)
#downloaded_files = dl_manager.download_download_and_extract(files_to_download)#downloaded_files = dl_manager.download_download_and_exctract(files_to_download)#
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": "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(".txt"):
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]")
#print(segs[6].strip())
yield i, {_DOCUMENT: segs[8].strip(), _SUMMARY: segs[6].strip(), _ID: i} |