xsum.py

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."""
        print(self.info.splits)
        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()],
                    }