Datasets:

taln-ls2n
/

kptimes

	"""KPTimes benchmark dataset for keyphrase extraction an generation."""


	import csv
	import json
	import os

	import datasets


	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@inproceedings{gallina-etal-2019-kptimes,
	title = "{KPT}imes: A Large-Scale Dataset for Keyphrase Generation on News Documents",
	author = "Gallina, Ygor and
	Boudin, Florian and
	Daille, Beatrice",
	booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
	month = oct # "{--}" # nov,
	year = "2019",
	address = "Tokyo, Japan",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/W19-8617",
	doi = "10.18653/v1/W19-8617",
	pages = "130--135",
	abstract = "Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github.com/ygorg/KPTimes.",
	}
	"""

	# You can copy an official description
	_DESCRIPTION = """\
	KPTimes benchmark dataset for keyphrase extraction an generation.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = "https://aclanthology.org/W03-1028.pdf"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = "Apache 2.0 License"

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"test": "test.jsonl",
	"train": "train.jsonl",
	"dev": "dev.jsonl"
	}


	# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
	class KPTimes(datasets.GeneratorBasedBuilder):
	"""TODO: Short description of my dataset."""

	VERSION = datasets.Version("1.1.0")

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# 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="raw", version=VERSION, description="This part of my dataset covers the raw data."),
	]

	DEFAULT_CONFIG_NAME = "raw" # 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
	if self.config.name == "raw": # This is the name of the configuration selected in BUILDER_CONFIGS above
	features = datasets.Features(
	{
	"id": datasets.Value("string"),
	"title": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	"keyphrases": datasets.features.Sequence(datasets.Value("string")),
	"prmu": datasets.features.Sequence(datasets.Value("string")),
	"date": datasets.Value("string"),
	"categories": datasets.features.Sequence(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, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# 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):
	# 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
	urls = _URLS
	data_dir = dl_manager.download_and_extract(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["train"]),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["test"]),
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir["dev"]),
	"split": "dev",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath, split):
	# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
	# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
	with open(filepath, encoding="utf-8") as f:
	for key, row in enumerate(f):
	data = json.loads(row)
	# Yields examples as (key, example) tuples
	yield key, {
	"id": data["id"],
	"title": data["title"],
	"abstract": data["abstract"],
	"keyphrases": data["keyphrases"],
	"prmu": data["prmu"],
	"date": data["date"],
	"categories": data["categories"],
	}