wiki_cat_sum.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""


import csv
import json
import re

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{perez2019generating,
  title={Generating Summaries with Topic Templates and Structured Convolutional Decoders},
  author={Perez-Beltrachini, Laura and Liu, Yang and Lapata, Mirella},
  booktitle={Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  pages={5107--5116},
  year={2019}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://datashare.ed.ac.uk/handle/10283/3368"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "CC BY-SA 3.0"

# TODO: Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLs = {
    "animal": {
        "train": "main_splits/train-animal.jsonl",
        "validation": "main_splits/valid-animal.jsonl",
        "test": "main_splits/test-animal.jsonl",
        "cs_abs": [
            "cs_abs/test-animal_nv_0.jsonl",
            "cs_abs/test-animal_nv_1.jsonl",
            "cs_abs/test-animal_nv_2.jsonl",
            "cs_abs/test-animal_nv_3.jsonl",
            "cs_abs/test-animal_nv_4.jsonl",
            "cs_abs/test-animal_nv_6.jsonl",
            "cs_abs/test-animal_nv_7.jsonl",
            "cs_abs/test-animal_nv_8.jsonl",
            "cs_abs/test-animal_nv_9.jsonl",
        ],
        "cs_tdiv": [
            "cs_tdiv/test-animal_tdiv_0.jsonl",
            "cs_tdiv/test-animal_tdiv_1.jsonl",
            "cs_tdiv/test-animal_tdiv_2.jsonl",
            "cs_tdiv/test-animal_tdiv_3.jsonl",
        ],
    },
    "company": {
        "train": "main_splits/train-company.jsonl",
        "validation": "main_splits/valid-company.jsonl",
        "test": "main_splits/test-company.jsonl",
        "cs_abs": [
            "cs_abs/test-company_nv_0.jsonl",
            "cs_abs/test-company_nv_1.jsonl",
            "cs_abs/test-company_nv_2.jsonl",
            "cs_abs/test-company_nv_3.jsonl",
            "cs_abs/test-company_nv_4.jsonl",
            "cs_abs/test-company_nv_6.jsonl",
            "cs_abs/test-company_nv_7.jsonl",
            "cs_abs/test-company_nv_8.jsonl",
            "cs_abs/test-company_nv_9.jsonl",
        ],
        "cs_tdiv": [
            "cs_tdiv/test-company_tdiv_0.jsonl",
            "cs_tdiv/test-company_tdiv_1.jsonl",
            "cs_tdiv/test-company_tdiv_2.jsonl",
            "cs_tdiv/test-company_tdiv_3.jsonl",
        ],
    },
    "film": {
        "train": "main_splits/train-film.jsonl",
        "validation": "main_splits/valid-film.jsonl",
        "test": "main_splits/test-film.jsonl",
        "cs_abs": [
            "cs_abs/test-film_nv_0.jsonl",
            "cs_abs/test-film_nv_1.jsonl",
            "cs_abs/test-film_nv_2.jsonl",
            "cs_abs/test-film_nv_3.jsonl",
            "cs_abs/test-film_nv_4.jsonl",
            "cs_abs/test-film_nv_6.jsonl",
            "cs_abs/test-film_nv_7.jsonl",
            "cs_abs/test-film_nv_8.jsonl",
            "cs_abs/test-film_nv_9.jsonl",
        ],
        "cs_tdiv": [
            "cs_tdiv/test-film_tdiv_0.jsonl",
            "cs_tdiv/test-film_tdiv_1.jsonl",
            "cs_tdiv/test-film_tdiv_2.jsonl",
            "cs_tdiv/test-film_tdiv_3.jsonl",
        ],
    },
}


def detokenize(text):
    """
    Untokenizing a text undoes the tokenizing operation, restoring
    punctuation and spaces to the places that people expect them to be.
    Ideally, `untokenize(tokenize(text))` should be identical to `text`,
    except for line breaks.
    """
    step1 = text.replace("`` ", '"').replace(" ''", '"').replace(". . .", "...")
    step2 = step1.replace(" ( ", " (").replace(" ) ", ") ")
    step3 = re.sub(r' ([.,:;?!%]+)([ \'"`])', r"\1\2", step2)
    step4 = re.sub(r" ([.,:;?!%]+)$", r"\1", step3)
    step5 = (
        step4.replace(" '", "'")
        .replace(" n't", "n't")
        .replace("can not", "cannot")
        .replace(" 've", "'ve")
    )
    step6 = step5.replace(" ` ", " '")
    return step6.strip()


class WikiCatSum(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.1.0")

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="animal", version=VERSION, description="Animal domain"
        ),
        datasets.BuilderConfig(
            name="company", version=VERSION, description="Company domain"
        ),
        datasets.BuilderConfig(name="film", version=VERSION, description="Film domain"),
    ]

    DEFAULT_CONFIG_NAME = "animal"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "gem_parent_id": datasets.Value("string"),
                "id": datasets.Value("string"),
                "title": datasets.Value("string"),
                "paragraphs": datasets.features.Sequence(datasets.Value("string")),
                "summary": datasets.features.Sequence(
                    {
                        "text": datasets.Value("string"),
                        "topic": datasets.Value("int16"),
                    }
                ),
                "target": datasets.Value("string"),
                "references": [
                    datasets.Value("string"),
                ],
            }
        )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        my_urls = _URLs[self.config.name]
        d_conf = dl_manager.download_and_extract(my_urls)
        challenge_sets = [
            ("challenge_test_abstractivity_%d" % (lvl), fname)
            for lvl, fname in enumerate(d_conf["cs_abs"])
        ] + [
            ("challenge_test_topic_diversity_%d" % (lvl), fname)
            for lvl, fname in enumerate(d_conf["cs_abs"])
        ]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": d_conf["train"],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": d_conf["validation"], "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": d_conf["test"],
                    "split": "validation",
                },
            ),
        ] + [
            datasets.SplitGenerator(
                name=challenge_split,
                gen_kwargs={
                    "filepath": filename,
                    "split": challenge_split,
                },
            )
            for challenge_split, filename in challenge_sets
        ]

    def _generate_examples(
        self,
        filepath,
        split,  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    ):
        """Yields examples as (key, example) tuples."""
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.

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

                # If summary is a list itself, we have multi-ref.
                if isinstance(data["summary"], list):
                    detok_targets = [
                        detokenize(" ".join(s["text"])) for s in data["summary"]
                    ]
                    data["target"] = detok_targets[0]
                    data["references"] = detok_targets
                elif isinstance(data["summary"]["text"], list):
                    detok_target = detokenize(" ".join(data["summary"]["text"]))
                    data["target"] = detok_target
                    data["references"] = [detok_target]
                # elif isinstance(data["summary"]["text"], str):
                #   detok_target = detokenize(data["summary"]["text"])
                else:
                    print(data["summary"])
                    exit()
                data["gem_parent_id"] = f"{self.config.name}-{split}-{id_+1}"
                data["gem_id"] = f"{self.config.name}-{split}-{id_+1}"
                yield id_, data