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Delete loading script

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  1. pubmed-summarization.py +0 -129
pubmed-summarization.py DELETED
@@ -1,129 +0,0 @@
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- import json
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- import os
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-
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- import datasets
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- from datasets.tasks import TextClassification
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-
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- _CITATION = None
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-
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-
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- _DESCRIPTION = """
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- PubMed dataset for summarization.
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- From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
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- See: https://aclanthology.org/N18-2097.pdf
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- See: https://github.com/armancohan/long-summarization
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- """
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- _CITATION = """\
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- @inproceedings{cohan-etal-2018-discourse,
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- title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
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- author = "Cohan, Arman and
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- Dernoncourt, Franck and
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- Kim, Doo Soon and
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- Bui, Trung and
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- Kim, Seokhwan and
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- Chang, Walter and
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- Goharian, Nazli",
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- 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)",
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- month = jun,
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- year = "2018",
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- address = "New Orleans, Louisiana",
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- publisher = "Association for Computational Linguistics",
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- url = "https://aclanthology.org/N18-2097",
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- doi = "10.18653/v1/N18-2097",
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- pages = "615--621",
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- 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.",
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- }
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- """
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- _ABSTRACT = "abstract"
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- _ARTICLE = "article"
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-
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- class PubMedSummarizationConfig(datasets.BuilderConfig):
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- """BuilderConfig for PubMedSummarization."""
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-
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- def __init__(self, **kwargs):
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- """BuilderConfig for PubMedSummarization.
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- Args:
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(PubMedSummarizationConfig, self).__init__(**kwargs)
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-
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-
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- class PubMedSummarizationDataset(datasets.GeneratorBasedBuilder):
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- """PubMedSummarization Dataset."""
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-
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- _TRAIN_FILE = "train.zip"
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- _VAL_FILE = "val.zip"
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- _TEST_FILE = "test.zip"
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-
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- BUILDER_CONFIGS = [
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- PubMedSummarizationConfig(
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- name="section",
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- version=datasets.Version("1.0.0"),
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- description="PubMed dataset for summarization, concat sections",
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- ),
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- PubMedSummarizationConfig(
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- name="document",
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- version=datasets.Version("1.0.0"),
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- description="PubMed dataset for summarization, document",
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- ),
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- ]
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-
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- DEFAULT_CONFIG_NAME = "section"
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-
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- def _info(self):
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- # Should return a datasets.DatasetInfo object
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=datasets.Features(
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- {
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- _ARTICLE: datasets.Value("string"),
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- _ABSTRACT: datasets.Value("string"),
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- #"id": datasets.Value("string"),
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- }
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- ),
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- supervised_keys=None,
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- homepage="https://github.com/armancohan/long-summarization",
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- citation=_CITATION,
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- )
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-
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- def _split_generators(self, dl_manager):
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-
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- train_path = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt")
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- val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "val.txt")
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- test_path = os.path.join(dl_manager.download_and_extract(self._TEST_FILE), "test.txt")
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-
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
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- ),
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- ]
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-
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- def _generate_examples(self, filepath):
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- """Generate PubMedSummarization examples."""
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- with open(filepath, encoding="utf-8") as f:
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- for id_, row in enumerate(f):
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- data = json.loads(row)
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-
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- """
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- 'article_id': str,
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- 'abstract_text': List[str],
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- 'article_text': List[str],
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- 'section_names': List[str],
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- 'sections': List[List[str]]
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- """
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- if self.config.name == "document":
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- article = [d.strip() for d in data["article_text"]]
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- article = " ".join(article)
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- else:
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- article = [item.strip() for sublist in data["sections"] for item in sublist]
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- article = " \n ".join(article)
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-
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- abstract = [ab.replace("<S>", "").replace("</S>", "").strip() for ab in data["abstract_text"]]
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- abstract = " \n ".join(abstract)
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- yield id_, {"article": article, "abstract": abstract}