fs.py

# coding=utf-8
# Lint as: python3
"""The SCROLLS benchmark."""

import json
import os
import datasets
from datasets import load_dataset

_FS_CITATION = """
TBD
"""

_FS_DESCRIPTION = """
TBD
"""

_SUMM_SCREEN_DESCRIPTION = """
SummScreenFD (Chen  et al., 2021) is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
Given a transcript of a specific episode, the goal is to produce the episode's recap.
The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts. 
For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows, 
making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows. 
Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze."""


_GOV_REPORT_DESCRIPTION = """
GovReport (Huang et al., 2021) is a summarization dataset of reports addressing various national policy issues published by the 
Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets; 
for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively."""



_SUMM_SCREEN_CITATION = r"""
@misc{chen2021summscreen,
      title={SummScreen: A Dataset for Abstractive Screenplay Summarization}, 
      author={Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel},
      year={2021},
      eprint={2104.07091},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}"""

_GOV_REPORT_CITATION = r"""
@inproceedings{huang-etal-2021-efficient,
    title = "Efficient Attentions for Long Document Summarization",
    author = "Huang, Luyang  and
      Cao, Shuyang  and
      Parulian, Nikolaus  and
      Ji, Heng  and
      Wang, Lu",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-main.112",
    doi = "10.18653/v1/2021.naacl-main.112",
    pages = "1419--1436",
    abstract = "The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.",
}"""


class FSConfig(datasets.BuilderConfig):
    """BuilderConfig for FS."""

    def __init__(self, features, data_url, citation, url, max_source_length, **kwargs):
        """BuilderConfig for FS.
        Args:
          features: `list[string]`, list of the features that will appear in the
            feature dict. Should not include "label".
          data_url: `string`, url to download the zip file from.
          citation: `string`, citation for the data set.
          url: `string`, url for information about the data set.
          label_classes: `list[string]`, the list of classes for the label if the
            label is present as a string. Non-string labels will be cast to either
            'False' or 'True'.
          **kwargs: keyword arguments forwarded to super.
        """
        super(FSConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.features = features
        self.data_url = data_url
        self.citation = citation
        self.url = url
        self.max_source_length = max_source_length


class Fs(datasets.GeneratorBasedBuilder):
    """The SCROLLS benchmark."""

    features = ["id", "pid", "source", "target"]
    DEFAULT_WRITER_BATCH_SIZE = 1000  # because Narrative QA is a rather large dataset
    BUILDER_CONFIGS = [
        FSConfig(
            name="summ_screen_fd",
            description=_SUMM_SCREEN_DESCRIPTION,
            features=features,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/summ_screen_fd.zip",
            citation=_SUMM_SCREEN_CITATION,
            url="https://github.com/mingdachen/SummScreen",
            max_source_length=None,
        ),
        FSConfig(
            name="gov_report",
            description=_GOV_REPORT_CITATION,
            features=features,
            data_url="https://huggingface.co/datasets/tau/fs/resolve/main/data/gov_report.zip",
            citation=_GOV_REPORT_DESCRIPTION,
            url="https://gov-report-data.github.io/",
            max_source_length=None,
        ),
    ]

    def _info(self):
        features = {feature: datasets.Value("string") for feature in self.config.features}

        return datasets.DatasetInfo(
            description=_FS_DESCRIPTION + self.config.description,
            features=datasets.Features(features),
            homepage=self.config.url,
            citation=self.config.citation + "\n" + _FS_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(self.config.data_url)

        data_files = {} if self.config.data_files is not None else None
        if data_files is not None:
            for split, paths in self.config.data_files.items():
                data_files[split] = paths[0]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "train.jsonl"),
                    "split": datasets.Split.TRAIN,
                    "max_source_length": self.config.max_source_length,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "validation.jsonl"),
                    "split": datasets.Split.VALIDATION,
                    "max_source_length": self.config.max_source_length,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "data_file": os.path.join(dl_dir, "test.jsonl") if data_files is None else data_files["test"],
                    "split": datasets.Split.TEST,
                    "max_source_length": self.config.max_source_length,
                },
            ),
        ]

    def _generate_examples(self, data_file, split, max_source_length):
        with open(data_file, encoding="utf-8") as f:
            for line in f:
                row = json.loads(line)
                yield row["pid"], row


def _get_task_name_from_data_url(data_url):
    return data_url.split("/")[-1].split(".")[0]


if __name__ == '__main__':
    dataset = load_dataset("tau/fs", "summ_screen_fd", max_source_length=512)
    x = 5
#    builder = Scrolls("scrolls", "summ_screen_fd")
#    builder.download_and_prepare()
#    dataset = builder.as_dataset("validation")
#    x = 5