Datasets:

GEM
/

xsum

	import json
	import os

	import datasets

	_CITATION = """\
	@inproceedings{narayan-etal-2018-dont,
	title = "Don{'}t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization",
	author = "Narayan, Shashi and
	Cohen, Shay B. and
	Lapata, Mirella",
	booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
	month = oct # "-" # nov,
	year = "2018",
	address = "Brussels, Belgium",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/D18-1206",
	doi = "10.18653/v1/D18-1206",
	pages = "1797--1807",
	abstract = "We introduce {``}extreme summarization{''}, a new single-document summarization task which does not favor extractive strategies and calls for an abstractive modeling approach. The idea is to create a short, one-sentence news summary answering the question {``}What is the article about?{''}. We collect a real-world, large-scale dataset for this task by harvesting online articles from the British Broadcasting Corporation (BBC). We propose a novel abstractive model which is conditioned on the article{'}s topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans.",
	}
	"""

	_DESCRIPTION = """\
	This is the XSUM subset of the GEM benchmark.
	"""
	_URLs = {
	"data": "http://bollin.inf.ed.ac.uk/public/direct/XSUM-EMNLP18-Summary-Data-Original.tar.gz",
	"splits": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_xsum_confidence_0.8.json",
	"challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/xsum.zip",
	}

	_XSUM_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):

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="xsum",
	version=datasets.Version("1.0.0"),
	description="",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"gem_parent_id": datasets.Value("string"),
	"xsum_id": datasets.Value("string"),
	"document": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	}
	),
	supervised_keys=None,
	homepage="",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	dl_dir = dl_manager.download_and_extract(_URLs)
	challenge_sets = [
	("challenge_train_sample", "train_xsum_RandomSample500.json"),
	("challenge_validation_sample", "validation_xsum_RandomSample500.json"),
	("challenge_test_backtranslation", "test_xsum_BackTranslation500.json"),
	(
	"challenge_test_bfp_02",
	"test_xsum_ButterFingersPerturbation_p=0.02_500.json",
	),
	(
	"challenge_test_bfp_05",
	"test_xsum_ButterFingersPerturbation_p=0.05_500.json",
	),
	("challenge_test_nopunc", "test_xsum_WithoutPunctuation500.json"),
	("challenge_test_covid", f"en_test_covid19.jsonl"),
	]
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": dl_dir["splits"],
	"split": "train",
	"filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": dl_dir["splits"],
	"split": "validation",
	"filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": dl_dir["splits"],
	"split": "test",
	"filepaths": os.path.join(dl_dir["data"], "bbc-summary-data"),
	},
	),
	] + [
	datasets.SplitGenerator(
	name=challenge_split,
	gen_kwargs={
	"filepath": os.path.join(dl_dir["challenge_set"], "xsum", filename),
	"split": challenge_split,
	},
	)
	for challenge_split, filename in challenge_sets
	]

	def _generate_examples(self, filepath, split, filepaths=None):
	"""Yields examples."""
	if "challenge" in split:
	if "covid" in split:
	with open(filepath, encoding="utf-8") as f:
	id_ = -1
	for line in f:
	data = json.loads(line)
	id_ += 1
	yield id_, {
	"gem_id": f"{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"{self.config.name}-{split}-{id_}",
	"xsum_id": data["url"],
	"document": data["text"],
	"target": data["summary"],
	"references": [] if split == "train" else [data["summary"]],
	}
	else:
	exples = json.load(open(filepath, encoding="utf-8"))
	if isinstance(exples, dict):
	assert len(exples) == 1, "multiple entries found"
	exples = list(exples.values())[0]
	for id_, exple in enumerate(exples):
	exple["gem_parent_id"] = exple["gem_id"]
	exple["gem_id"] = f"{self.config.name}-{split}-{id_}"
	yield id_, exple
	else:
	with open(filepath, "r", encoding="utf-8") as f:
	split_ids = json.load(f)
	for id_, i in enumerate(split_ids[split]):
	with open(
	os.path.join(filepaths, i + ".summary"), "r", encoding="utf-8"
	) as f:
	text = "".join(
	[
	line
	for line in f.readlines()
	if line not in _XSUM_REMOVE_LINES and line.strip()
	]
	)
	segs = text.split("[SN]")
	yield id_, {
	"gem_id": f"{self.config.name}-{split}-{id_}",
	"gem_parent_id": f"{self.config.name}-{split}-{id_}",
	"xsum_id": i,
	"document": segs[8].strip(),
	"target": segs[6].strip(),
	"references": [] if split == "train" else [segs[6].strip()],
	}