Datasets:

GEM
/

wiki_cat_sum

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.
	"""TODO: Add a description here."""


	import csv
	import json
	import re

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{perez2019generating,
	title={Generating Summaries with Topic Templates and Structured Convolutional Decoders},
	author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella},
	booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
	pages={5107--5116},
	year={2019}
	}
	"""

	# TODO: Add description of the dataset here
	# You can copy an official description
	_DESCRIPTION = """\
	Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/3368"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "CC BY-SA 3.0"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace dataset library don't host the datasets but only point to the original files
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLs = {
	"animal": {
	"train": "main_splits/train-animal.jsonl",
	"validation": "main_splits/valid-animal.jsonl",
	"test": "main_splits/test-animal.jsonl",
	"cs_abs": [
	"cs_abs/test-animal_nv_0.jsonl",
	"cs_abs/test-animal_nv_1.jsonl",
	"cs_abs/test-animal_nv_2.jsonl",
	"cs_abs/test-animal_nv_3.jsonl",
	"cs_abs/test-animal_nv_4.jsonl",
	"cs_abs/test-animal_nv_6.jsonl",
	"cs_abs/test-animal_nv_7.jsonl",
	"cs_abs/test-animal_nv_8.jsonl",
	"cs_abs/test-animal_nv_9.jsonl",
	],
	"cs_tdiv": [
	"cs_tdiv/test-animal_tdiv_0.jsonl",
	"cs_tdiv/test-animal_tdiv_1.jsonl",
	"cs_tdiv/test-animal_tdiv_2.jsonl",
	"cs_tdiv/test-animal_tdiv_3.jsonl",
	],
	},
	"company": {
	"train": "main_splits/train-company.jsonl",
	"validation": "main_splits/valid-company.jsonl",
	"test": "main_splits/test-company.jsonl",
	"cs_abs": [
	"cs_abs/test-company_nv_0.jsonl",
	"cs_abs/test-company_nv_1.jsonl",
	"cs_abs/test-company_nv_2.jsonl",
	"cs_abs/test-company_nv_3.jsonl",
	"cs_abs/test-company_nv_4.jsonl",
	"cs_abs/test-company_nv_6.jsonl",
	"cs_abs/test-company_nv_7.jsonl",
	"cs_abs/test-company_nv_8.jsonl",
	"cs_abs/test-company_nv_9.jsonl",
	],
	"cs_tdiv": [
	"cs_tdiv/test-company_tdiv_0.jsonl",
	"cs_tdiv/test-company_tdiv_1.jsonl",
	"cs_tdiv/test-company_tdiv_2.jsonl",
	"cs_tdiv/test-company_tdiv_3.jsonl",
	],
	},
	"film": {
	"train": "main_splits/train-film.jsonl",
	"validation": "main_splits/valid-film.jsonl",
	"test": "main_splits/test-film.jsonl",
	"cs_abs": [
	"cs_abs/test-film_nv_0.jsonl",
	"cs_abs/test-film_nv_1.jsonl",
	"cs_abs/test-film_nv_2.jsonl",
	"cs_abs/test-film_nv_3.jsonl",
	"cs_abs/test-film_nv_4.jsonl",
	"cs_abs/test-film_nv_6.jsonl",
	"cs_abs/test-film_nv_7.jsonl",
	"cs_abs/test-film_nv_8.jsonl",
	"cs_abs/test-film_nv_9.jsonl",
	],
	"cs_tdiv": [
	"cs_tdiv/test-film_tdiv_0.jsonl",
	"cs_tdiv/test-film_tdiv_1.jsonl",
	"cs_tdiv/test-film_tdiv_2.jsonl",
	"cs_tdiv/test-film_tdiv_3.jsonl",
	],
	},
	}


	def detokenize(text):
	"""
	Untokenizing a text undoes the tokenizing operation, restoring
	punctuation and spaces to the places that people expect them to be.
	Ideally, `untokenize(tokenize(text))` should be identical to `text`,
	except for line breaks.
	"""
	step1 = text.replace("`` ", '"').replace(" ''", '"').replace(". . .", "...")
	step2 = step1.replace(" ( ", " (").replace(" ) ", ") ")
	step3 = re.sub(r' ([.,:;?!%]+)([ \'"`])', r"\1\2", step2)
	step4 = re.sub(r" ([.,:;?!%]+)$", r"\1", step3)
	step5 = (
	step4.replace(" '", "'")
	.replace(" n't", "n't")
	.replace("can not", "cannot")
	.replace(" 've", "'ve")
	)
	step6 = step5.replace(" ` ", " '")
	return step6.strip()


	class WikiCatSum(datasets.GeneratorBasedBuilder):
	"""A summarization dataset with multiple domains."""

	VERSION = datasets.Version("0.1.0")

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="animal", version=VERSION, description="Animal domain"
	),
	datasets.BuilderConfig(
	name="company", version=VERSION, description="Company domain"
	),
	datasets.BuilderConfig(name="film", version=VERSION, description="Film domain"),
	]

	DEFAULT_CONFIG_NAME = "animal" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
	features = datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"paragraphs": datasets.features.Sequence(datasets.Value("string")),
	"summary": datasets.features.Sequence(
	{
	"text": datasets.Value("string"),
	"topic": datasets.Value("int16"),
	}
	),
	"target": datasets.Value("string"),
	"references": [
	datasets.Value("string"),
	],
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features,
	# specify them here. They'll be used if as_supervised=True in
	# builder.as_dataset.
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
	# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

	# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
	# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
	# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
	my_urls = _URLs[self.config.name]
	d_conf = dl_manager.download_and_extract(my_urls)
	challenge_sets = [
	("challenge_test_abstractivity_%d" % (lvl), fname)
	for lvl, fname in enumerate(d_conf["cs_abs"])
	] + [
	("challenge_test_topic_diversity_%d" % (lvl), fname)
	for lvl, fname in enumerate(d_conf["cs_abs"])
	]

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": d_conf["train"],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": d_conf["validation"], "split": "test"},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": d_conf["test"],
	"split": "validation",
	},
	),
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": filename,
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(
	self,
	filepath,
	split, # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	):
	"""Yields examples as (key, example) tuples."""
	# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is here for legacy reason (tfds) and is not important in itself.

	with open(filepath, encoding="utf-8") as f:
	for id_, row in enumerate(f):
	data = json.loads(row)
	data["paragraphs"] = [detokenize(p) for p in data["paragraphs"]]

	# If summary is a list itself, we have multi-ref.
	if isinstance(data["summary"], list):
	detok_targets = " ".join([
	detokenize(s["text"]) for s in data["summary"]
	])

	data["target"] = detok_targets
	data["references"] = [detok_targets]
	# elif isinstance(data["summary"]["text"], list):
	# detok_target = detokenize(" ".join(data["summary"]["text"]))
	# print("\n\n\n\n", detok_target)
	# exit()
	# data["target"] = detok_target
	# data["references"] = [detok_target]
	# elif isinstance(data["summary"]["text"], str):
	# detok_target = detokenize(data["summary"]["text"])
	else:
	print(data["summary"])
	exit()
	data["gem_parent_id"] = f"{self.config.name}-{split}-{id_+1}"
	data["gem_id"] = f"{self.config.name}-{split}-{id_+1}"
	yield id_, data