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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
"""WikiHow Datasets."""
import csv
import os
import re
import datasets
_CITATION = """\
@misc{koupaee2018wikihow,
title={WikiHow: A Large Scale Text Summarization Dataset},
author={Mahnaz Koupaee and William Yang Wang},
year={2018},
eprint={1810.09305},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """\
WikiHow is a new large-scale dataset using the online WikiHow
(http://www.wikihow.com/) knowledge base.
There are two features:
- text: wikihow answers texts.
- headline: bold lines as summary.
There are two separate versions:
- all: consisting of the concatenation of all paragraphs as the articles and
the bold lines as the reference summaries.
- sep: consisting of each paragraph and its summary.
Download "wikihowAll.csv" and "wikihowSep.csv" from
https://github.com/mahnazkoupaee/WikiHow-Dataset and place them in manual folder
https://www.tensorflow.org/datasets/api_docs/python/tfds/download/DownloadConfig.
Train/validation/test splits are provided by the authors.
Preprocessing is applied to remove short articles
(abstract length < 0.75 article length) and clean up extra commas.
"""
_DOCUMENT = "text"
_SUMMARY = "headline"
_URLS = {
"train": "https://raw.githubusercontent.com/mahnazkoupaee/WikiHow-Dataset/master/all_train.txt",
"validation": "https://raw.githubusercontent.com/mahnazkoupaee/WikiHow-Dataset/master/all_val.txt",
"test": "https://raw.githubusercontent.com/mahnazkoupaee/WikiHow-Dataset/master/all_test.txt",
}
class WikihowConfig(datasets.BuilderConfig):
"""BuilderConfig for Wikihow."""
def __init__(self, filename=None, **kwargs):
"""BuilderConfig for Wikihow.
Args:
filename: filename of different configs for the dataset.
**kwargs: keyword arguments forwarded to super.
"""
# Version 1.1.0 remove empty document and summary strings.
# Version 1.2.0 add train validation test split, add cleaning & filtering.
super(WikihowConfig, self).__init__(version=datasets.Version("1.2.0"), **kwargs)
self.filename = filename
class Wikihow(datasets.GeneratorBasedBuilder):
"""WikiHow: A Large Scale Text Summarization Dataset."""
BUILDER_CONFIGS = [
WikihowConfig(
name="all",
filename="wikihowAll.csv",
description="Use the concatenation of all paragraphs as the articles"
" and the bold lines as the reference summaries",
),
WikihowConfig(name="sep", filename="wikihowSep.csv", description="use each paragraph and its summary."),
]
@property
def manual_download_instructions(self):
return """\
You need to manually download one of the wikihow files. An overview of which files to download can be seen at https://github.com/mahnazkoupaee/WikiHow-Dataset.
You need to download one the following two data files manually, depending on the version you want:
1) all: https://ucsb.app.box.com/s/ap23l8gafpezf4tq3wapr6u8241zz358 and save the file under <path/to/folder>/wikihowAll.csv
2) sep: https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag and save the file under <path/to/folder>/wikihowSep.csv
The <path/to/folder> can e.g. be "~/manual_wikihow_data".
Wikihow can then be loaded for example using the following command `datasets.load_dataset("wikihow", "all", data_dir="<path/to/folder>")`.
"""
def _info(self):
feature_names = [_DOCUMENT, _SUMMARY, "title"]
if self.config.name == "sep":
feature_names.extend(["overview", "sectionLabel"])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({k: datasets.Value("string") for k in feature_names}),
supervised_keys=None,
homepage="https://github.com/mahnazkoupaee/WikiHow-Dataset",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_path = dl_manager.download_and_extract(_URLS)
titles = {k: set() for k in dl_path}
for k, path in dl_path.items():
with open(path, encoding="utf-8") as f:
for line in f:
titles[k].add(line.strip())
path_to_manual_file = os.path.join(
os.path.abspath(os.path.expanduser(dl_manager.manual_dir)), self.config.filename
)
if not os.path.exists(path_to_manual_file):
raise FileNotFoundError(
f"{path_to_manual_file} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('wikihow', data_dir=...)` that includes a file name {self.config.filename}. Manual download instructions: {self.manual_download_instructions})"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"path": path_to_manual_file,
"title_set": titles["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"path": path_to_manual_file,
"title_set": titles["validation"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"path": path_to_manual_file,
"title_set": titles["test"],
},
),
]
def _generate_examples(self, path=None, title_set=None):
"""Yields examples."""
with open(path, encoding="utf-8") as f:
reader = csv.reader(f)
headers = next(reader)
if self.config.name == "all" and headers != ["headline", "title", "text"]:
raise ValueError("Mismatched header in WikiAll.txt")
if self.config.name == "sep" and headers != ["overview", "headline", "text", "sectionLabel", "title"]:
raise ValueError("Mismatched header in WikiSep.txt")
key2id = {key: i for i, key in enumerate(headers)}
for i, line in enumerate(reader):
# skip empty line or insufficient line.
if len(line) == len(key2id):
summary = line[key2id[_SUMMARY]].strip()
document = line[key2id[_DOCUMENT]].strip()
summary, document = _filter_and_clean(summary, document)
if summary and document:
if line[key2id["title"]].strip().replace(" ", "") in title_set:
d = {k: line[v].strip() for k, v in key2id.items() if k not in [_SUMMARY, _DOCUMENT]}
d[_DOCUMENT] = document
d[_SUMMARY] = summary
yield i, d
# This functions follow data processing acoording to original paper at
# https://github.com/mahnazkoupaee/WikiHow-Dataset/blob/master/process.py
def _filter_and_clean(abstract, article):
"""Remove short article and clean up commas in abstract and article."""
# a threshold is used to remove short articles with long summaries
# as well as articles with no summary
if len(abstract) < (0.75 * len(article)):
# remove extra commas in abstracts
abstract = abstract.replace(".,", ".")
# remove extra commas in articles
article = re.sub(r"[.]+[\n]+[,]", ".\n", article)
return abstract, article
else:
return "", ""
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