WCEP-10.py

import json
import os

import datasets
from datasets.tasks import TextClassification

_CITATION = None


_DESCRIPTION = """
 WCEP10 dataset for summarization.
 From paper: "A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
                Current Events Portal" by D. Gholipour et al."
 From paper: "PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document
                Summarization" by W. Xiao et al."

"""
_CITATION = """\
    @article{DBLP:journals/corr/abs-2005-10070,
    author    = {Demian Gholipour Ghalandari and
                Chris Hokamp and
                Nghia The Pham and
                John Glover and
                Georgiana Ifrim},
    title     = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
                Current Events Portal},
    journal   = {CoRR},
    volume    = {abs/2005.10070},
    year      = {2020},
    url       = {https://arxiv.org/abs/2005.10070},
    eprinttype = {arXiv},
    eprint    = {2005.10070},
    timestamp = {Fri, 22 May 2020 16:21:28 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }


    @article{DBLP:journals/corr/abs-2110-08499,
    author    = {Wen Xiao and
                Iz Beltagy and
                Giuseppe Carenini and
                Arman Cohan},
    title     = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document
                Summarization},
    journal   = {CoRR},
    volume    = {abs/2110.08499},
    year      = {2021},
    url       = {https://arxiv.org/abs/2110.08499},
    eprinttype = {arXiv},
    eprint    = {2110.08499},
    timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_ABSTRACT = "summary"
_ARTICLE = "document"

class WCEP10SummarizationConfig(datasets.BuilderConfig):
    """BuilderConfig for WCEP10Summarization."""

    def __init__(self, **kwargs):
        """BuilderConfig for WCEP10Summarization.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(WCEP10SummarizationConfig, self).__init__(**kwargs)


class WCEP10SummarizationDataset(datasets.GeneratorBasedBuilder):
    """WCEP10Summarization Dataset."""
    
    _TRAIN_FILE = "train.zip"
    _VAL_FILE = "val.zip"
    _TEST_FILE = "test.zip"

    BUILDER_CONFIGS = [
        WCEP10SummarizationConfig(
            name="newline",
            version=datasets.Version("1.0.0"),
            description="WCEP10 dataset for summarization, concat sections",
        ),
        WCEP10SummarizationConfig(
            name="roberta",
            version=datasets.Version("1.0.0"),
            description="WCEP10 dataset for summarization, document",
        ),
        WCEP10SummarizationConfig(
            name="bert",
            version=datasets.Version("1.0.0"),
            description="WCEP10 dataset for summarization, document",
        ),
        WCEP10SummarizationConfig(
            name="list",
            version=datasets.Version("1.0.0"),
            description="WCEP10 dataset for summarization, document",
        ),
    ]

    DEFAULT_CONFIG_NAME = "roberta"

    def _info(self):
        # Should return a datasets.DatasetInfo object
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    _ARTICLE: datasets.Sequence(datasets.Value("string")) if self.config.name == "list" else datasets.Value("string"),
                    _ABSTRACT: datasets.Value("string"),
                    #"id": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/allenai/PRIMER",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        train_path = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt")
        val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "val.txt")
        test_path = os.path.join(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 WCEP10Summarization examples."""
        if self.config.name == "newline":
            join_ = "\n"
        elif self.config.name == "roberta":
            join_ = "</s>"
        elif self.config.name == "bert": 
            join_ = "[SEP]"

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)

                """
                'summary': str,
                'document': List[str],
                """
                document = data["document"]
                if self.config.name != "list":
                    document = join_.join(document)
                summary = data["summary"]
                yield id_, {"document": document, "summary": summary}