bart_newsroom.py

# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""NEWSROOM Dataset."""


import json
import os
from bs4 import BeautifulSoup
import re

import datasets


_CITATION = """
@inproceedings{N18-1065,
  author    = {Grusky, Max and Naaman, Mor and Artzi, Yoav},
  title     = {NEWSROOM: A Dataset of 1.3 Million Summaries
               with Diverse Extractive Strategies},
  booktitle = {Proceedings of the 2018 Conference of the
               North American Chapter of the Association for
               Computational Linguistics: Human Language Technologies},
  year      = {2018},
}
"""

_DESCRIPTION = """
NEWSROOM is a large dataset for training and evaluating summarization systems.
It contains 1.3 million articles and summaries written by authors and
editors in the newsrooms of 38 major publications.
Dataset features includes:
  - text: Input news text.
  - summary: Summary for the news.
This dataset can be downloaded upon requests. Unzip all the contents
"train.jsonl, dev.josnl, test.jsonl" to the tfds folder.
"""

_DOCUMENT = "text"
_SUMMARY = "summary"


_DATA_DIR = os.path.join(
    os.path.expanduser("~"), ".cache/huggingface/datasets/newsroom"
)


DM_SINGLE_CLOSE_QUOTE = "\u2019"  # unicode
DM_DOUBLE_CLOSE_QUOTE = "\u201d"
# acceptable ways to end a sentence
END_TOKENS = [
    ".",
    "!",
    "?",
    "...",
    "'",
    "`",
    '"',
    DM_SINGLE_CLOSE_QUOTE,
    DM_DOUBLE_CLOSE_QUOTE,
    ")",
]


def _process_document_and_summary(document, summary):
    """Process document to remove double newlines and process summary to remove html tags and
    encode to utf-8.

    References:
        (1) https://stackoverflow.com/a/12982689/13448382
        (2) https://stackoverflow.com/a/67560556/13448382
        (3) https://docs.python.org/3/howto/unicode.html#the-unicode-type
        (4) https://stackoverflow.com/a/11566398/13448382
        (5) https://huggingface.co/datasets/cnn_dailymail/blob/2d2c6100ccd17c0b215f85c38e36c4e7a5746425/cnn_dailymail.py#L155
    """
    # process document
    document = re.sub(r"\n+", " ", document).strip()

    # process summary
    try:
        summary = summary.encode("latin1")
    except:
        summary = summary.encode("utf-8")
    finally:
        summary = BeautifulSoup(summary.decode("utf-8", "ignore"), "lxml").text
    summary = summary.replace("“", '"').replace("”", '"')
    summary = re.sub(r"\n+", " ", summary)
    summary = re.sub(r"\s+", " ", summary)
    summary = re.sub(r"\\'", "'", summary)
    summary = re.sub("''", '"', summary)
    summary = re.sub("\xa0", " ", summary)
    summary = re.sub(r'\."', '".', summary)
    summary = re.sub(r'\,"', '",', summary).strip()
    if summary and summary[-1] not in END_TOKENS:
        summary += "."

    return document, summary


class Newsroom(datasets.GeneratorBasedBuilder):
    """NEWSROOM Dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = {k: datasets.Value("string") for k in [_DOCUMENT, _SUMMARY]}
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(features),
            supervised_keys=(_DOCUMENT, _SUMMARY),
            homepage="https://lil.nlp.cornell.edu/newsroom/index.html",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"input_file": os.path.join(_DATA_DIR, "train.jsonl")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"input_file": os.path.join(_DATA_DIR, "dev.jsonl")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"input_file": os.path.join(_DATA_DIR, "test.jsonl")},
            ),
        ]

    def _generate_examples(self, input_file=None):
        """Yields examples."""
        idx = 0
        with open(input_file, encoding="utf-8") as f:
            for line in f:
                d = json.loads(line)
                document, summary = _process_document_and_summary(
                    d[_DOCUMENT], d[_SUMMARY]
                )
                if not summary or not document:
                    continue
                yield idx, {_DOCUMENT: document, _SUMMARY: summary}
                idx += 1