Datasets:

GEM
/

xmediasum

	import json
	import datasets


	_CITATION = """\
	@inproceedings{wang-etal-2022-clidsum,
	title = "{C}lid{S}um: A Benchmark Dataset for Cross-Lingual Dialogue Summarization",
	author = "Wang, Jiaan and
	Meng, Fandong and
	Lu, Ziyao and
	Zheng, Duo and
	Li, Zhixu and
	Qu, Jianfeng and
	Zhou, Jie",
	booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
	month = dec,
	year = "2022",
	address = "Abu Dhabi, United Arab Emirates",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2022.emnlp-main.526",
	pages = "7716--7729",
	abstract = "We present ClidSum, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents. It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages. Based on the proposed ClidSum, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively. We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on ClidSum to provide deeper analyses. Furthermore, we propose mDialBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language. Experimental results show the superiority of mDialBART, as an end-to-end model, outperforms strong pipeline models on ClidSum. Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research. We have released the dataset and code at https://github.com/krystalan/ClidSum.",
	}
	"""


	_DESCRIPTION = """\
	We present XMediaSum, a cross-lingual dialogue summarization dataset with 40K English(dialogues)->Chinese(summaries) and 40K English (dialogues)->German(summaries) samples. XMediaSum is created by manually translating the English summaries of MediaSum (a English monolingual dialogue summarization dataset) to both Chinese and German.
	"""

	_HOMEPAGE = "https://github.com/krystalan/ClidSum"

	_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"

	_URLS = {
	"train": "https://huggingface.co/datasets/Krystalan/xmediasum/resolve/main/data/train.json",
	"dev": "https://huggingface.co/datasets/Krystalan/xmediasum/resolve/main/data/val.json",
	"test": "https://huggingface.co/datasets/Krystalan/xmediasum/resolve/main/data/test.json"
	}



	class xmediasum(datasets.GeneratorBasedBuilder):

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

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"dialogue": datasets.Value("string"),
	"summary": datasets.Value("string"),
	"summary_de": datasets.Value("string"),
	"summary_zh": datasets.Value("string")
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE,
	version=self.VERSION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	downloaded_files = dl_manager.download_and_extract(_URLS)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepath": downloaded_files["train"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": downloaded_files["test"],
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": downloaded_files["dev"],
	},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples as (key, example) tuples."""
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)

	for idx, item in enumerate(data):
	yield idx, item