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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()],
}
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