Datasets:

GEM
/

SciDuet

	import json
	import os

	import datasets

	_CITATION = """\
	@inproceedings{sun-etal-2021-d2s,
	title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
	author = "Sun, Edward and
	Hou, Yufang and
	Wang, Dakuo and
	Zhang, Yunfeng and
	Wang, Nancy X. R.",
	booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
	month = June,
	year = "2021",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.naacl-main.111",
	doi = "10.18653/v1/2021.naacl-main.111",
	pages = "1405--1418",
	}
	"""

	_DESCRIPTION = """\
	SciDuet is the first publicaly available dataset for the challenging task of document2slides generation,
	The dataset integrated into GEM is the ACL portion of the whole dataset described in "https://aclanthology.org/2021.naacl-main.111.pdf".
	It contains the full Dev and Test sets, and a portion of the Train dataset.
	We additionally create a challenge dataset in which the slide titles do not match with the
	section headers of the corresponding paper.
	Note that although we cannot release the whole training dataset due to copyright issues, researchers can still
	use our released data procurement code from https://github.com/IBM/document2slides
	to generate the training dataset from the online ICML/NeurIPS anthologies.
	In the released dataset, the original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits.
	The text contents from multiple slides that correspond to the same slide title are mreged.
	"""




	class SciDuetConfig(datasets.BuilderConfig):
	"""BuilderConfig for SciDuet."""

	def __init__(self, **kwargs):
	"""BuilderConfig for SciDuet.
	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(SciDuetConfig, self).__init__(**kwargs)


	class SciDuet(datasets.GeneratorBasedBuilder):

	VERSION = datasets.Version("1.0.0")

	# BUILDER_CONFIGS = [
	# SciDuetConfig(name="gem_data_split", version=VERSION_1, description="SciDuet - GEM version 1"),
	# ]
	#
	# DEFAULT_CONFIG_NAME = "gem_data_split"

	def _info(self):

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"gem_id": datasets.Value("string"),
	"paper_id": datasets.Value("string"),
	"paper_title": datasets.Value("string"),
	"paper_abstract": datasets.Value("string"),
	"paper_content": datasets.features.Sequence({
	"paper_content_id": datasets.Value("int32"),
	"paper_content_text": datasets.Value("string"),
	}),
	"paper_headers": datasets.features.Sequence({
	"paper_header_number": datasets.Value("string"),
	"paper_header_content": datasets.Value("string"),
	}),

	"slide_id": datasets.Value("string"),
	"slide_title": datasets.Value("string"),
	"slide_content_text": datasets.Value("string"),
	"target": datasets.Value("string"),
	"references": [datasets.Value("string")],
	}
	),
	supervised_keys=None,
	license="Apache License 2.0",
	citation=_CITATION,
	)


	def _split_generators(self, dl_manager):
	_URL = "https://huggingface.co/datasets/GEM/SciDuet/"
	_URLs = {
	"train": "train.json",
	"validation": "validation.json",
	"test": "test.json",
	"challenge_set": "challenge_woSectionHeader.json",
	}
	downloaded_files = dl_manager.download_and_extract(_URLs)

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

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	data = json.load(f)["data"]
	for item in data:
	gem_id = item["gem_id"]
	paper_id = item["paper_id"]
	paper_title = item["paper_title"]
	paper_abstract = item["paper"]["abstract"]
	paper_content_ids = [text["id"] for text in item["paper"]["text"]]
	paper_content_texts = [text["string"] for text in item["paper"]["text"]]
	paper_header_numbers = [header["n"] for header in item["paper"]["headers"]]
	paper_header_contents = [header["section"] for header in item["paper"]["headers"]]
	for j in item["slides"]:
	id_ = gem_id + "#" + "paper-" + paper_id + "#" + "slide-" + str(j)
	slide_title = item["slides"][j]["title"]
	slide_content_text = '\n'.join(item["slides"][j]["text"])

	yield id_, {
	"gem_id": id_,
	"paper_id": paper_id,
	"paper_title": paper_title,
	"paper_abstract": paper_abstract,
	"paper_content": {"paper_content_id":paper_content_ids, "paper_content_text":paper_content_texts},
	"paper_headers": {"paper_header_number":paper_header_numbers, "paper_header_content":paper_header_contents},
	"slide_id": id_,
	"slide_title": slide_title,
	"slide_content_text": slide_content_text,
	"target": slide_content_text,
	"references": [] if split == "train" else [slide_content_text],
	}