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import json
import os
import datasets
from datasets.tasks import TextClassification
_CITATION = None
_DESCRIPTION = """
PubMed dataset for summarization.
From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
See: https://aclanthology.org/N18-2097.pdf
See: https://github.com/armancohan/long-summarization
"""
_CITATION = """\
@inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, Nazli",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2097",
doi = "10.18653/v1/N18-2097",
pages = "615--621",
abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
}
"""
_ABSTRACT = "abstract"
_ARTICLE = "article"
class PubMedSummarizationConfig(datasets.BuilderConfig):
"""BuilderConfig for PubMedSummarization."""
def __init__(self, **kwargs):
"""BuilderConfig for PubMedSummarization.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PubMedSummarizationConfig, self).__init__(**kwargs)
class PubMedSummarizationDataset(datasets.GeneratorBasedBuilder):
"""PubMedSummarization Dataset."""
_TRAIN_FILE = "train.zip"
_VAL_FILE = "val.zip"
_TEST_FILE = "test.zip"
BUILDER_CONFIGS = [
PubMedSummarizationConfig(
name="section",
version=datasets.Version("1.0.0"),
description="PubMed dataset for summarization, concat sections",
),
PubMedSummarizationConfig(
name="document",
version=datasets.Version("1.0.0"),
description="PubMed dataset for summarization, document",
),
]
DEFAULT_CONFIG_NAME = "section"
def _info(self):
# Should return a datasets.DatasetInfo object
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
_ARTICLE: datasets.Value("string"),
_ABSTRACT: datasets.Value("string"),
#"id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/armancohan/long-summarization",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(self._TRAIN_FILE) + "/train.txt"
val_path = dl_manager.download_and_extract(self._VAL_FILE) + "/val.txt"
test_path = dl_manager.download_and_extract(self._TEST_FILE) + "/test.txt"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
),
]
def _generate_examples(self, filepath):
"""Generate PubMedSummarization examples."""
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
"""
'article_id': str,
'abstract_text': List[str],
'article_text': List[str],
'section_names': List[str],
'sections': List[List[str]]
"""
if self.config.name == "document":
article = [d.strip() for d in data["article_text"]]
article = " ".join(article)
else:
article = [item.strip() for sublist in data["sections"] for item in sublist]
article = " \n ".join(article)
abstract = [ab.replace("<S>", "").replace("</S>", "").strip() for ab in data["abstract_text"]]
abstract = " \n ".join(abstract)
yield id_, {"article": article, "abstract": abstract}
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