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"""GovReport: The Government Report Long Document Summarization Dataset.""" |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@inproceedings{huang-etal-2021-efficient, |
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title = "Efficient Attentions for Long Document Summarization", |
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author = "Huang, Luyang and |
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Cao, Shuyang and |
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Parulian, Nikolaus and |
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Ji, Heng and |
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Wang, Lu", |
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booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = jun, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.naacl-main.112", |
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doi = "10.18653/v1/2021.naacl-main.112", |
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pages = "1419--1436", |
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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.", |
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} |
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""" |
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_DESCRIPTION = """\ |
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GovReport long document summarization dataset. |
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There are three configs: |
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- plain_text: plain text document-to-summary pairs |
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- plain_text_with_recommendations: plain text doucment-summary pairs, with "What GAO recommends" included in the summary |
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- structure: data with section structure |
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""" |
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_URL = "https://huggingface.co/datasets/launch/gov_report/resolve/main/data/" |
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_URLS = { |
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"gao_train": _URL + "gao_train.jsonl", |
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"gao_valid": _URL + "gao_valid.jsonl", |
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"gao_test": _URL + "gao_test.jsonl", |
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"crs_train": _URL + "crs_train.jsonl", |
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"crs_valid": _URL + "crs_valid.jsonl", |
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"crs_test": _URL + "crs_test.jsonl", |
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} |
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def _recursive_load(section, keep_letter=False, depth=0): |
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sections = [] |
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if section["section_title"] != "Letter" or (section["section_title"] == "Letter" and keep_letter): |
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sections.append({ |
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"title": " ".join(section["section_title"].strip().split()), |
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"paragraphs": "\n".join([" ".join(paragraph.strip().split()) for paragraph in section["paragraphs"]]), |
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"depth": depth |
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}) |
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for subsection in section["subsections"]: |
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child_sections = _recursive_load(subsection, keep_letter, depth + 1) |
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sections.extend(child_sections) |
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else: |
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for subsection in section["subsections"]: |
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child_sections = _recursive_load(subsection, keep_letter, depth) |
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sections.extend(child_sections) |
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return sections |
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class GovReportConfig(datasets.BuilderConfig): |
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"""BuilderConfig for GovReport.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for GovReport. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(GovReportConfig, self).__init__(**kwargs) |
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class GovReport(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.1") |
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DEFAULT_CONFIG_NAME = "plain_text" |
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BUILDER_CONFIGS = [ |
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GovReportConfig( |
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name="plain_text", |
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version=VERSION, |
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description="Plain text", |
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), |
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GovReportConfig( |
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name="plain_text_with_recommendations", |
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version=VERSION, |
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description="Plain text with GAO recommendations", |
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), |
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GovReportConfig( |
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name="structure", |
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version=VERSION, |
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description="structure data", |
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) |
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] |
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def _info(self): |
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if self.config.name in ["plain_text", "plain_text_with_recommendations"]: |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document": datasets.Value("string"), |
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"summary": datasets.Value("string") |
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} |
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) |
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elif self.config.name == "structure": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"document_sections": datasets.features.Sequence( |
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{ |
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"title": datasets.Value("string"), |
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"paragraphs": datasets.Value("string"), |
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"depth": datasets.Value("int32"), |
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} |
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), |
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"summary_sections": datasets.features.Sequence( |
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{ |
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"title": datasets.Value("string"), |
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"paragraphs": datasets.Value("string"), |
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} |
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), |
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} |
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) |
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else: |
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raise ValueError("Unsupported config name {}".format(self.config.name)) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage="", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"gao_filepath": downloaded_files["gao_train"], "crs_filepath": downloaded_files["crs_train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"gao_filepath": downloaded_files["gao_valid"], "crs_filepath": downloaded_files["crs_valid"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"gao_filepath": downloaded_files["gao_test"], "crs_filepath": downloaded_files["crs_test"]}), |
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] |
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def _generate_examples(self, gao_filepath, crs_filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info(f"generating examples from = (GAO) {gao_filepath} and (CRS) {crs_filepath}") |
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with open(gao_filepath, "r") as f: |
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for line in f: |
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line = line.strip() |
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if not line: |
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continue |
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data = json.loads(line) |
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_id = 'GAO_' + data["id"] |
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document_sections = [] |
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for lv1_section in data["report"]: |
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document_sections.extend(_recursive_load(lv1_section, keep_letter=False, depth=1)) |
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summary_sections = [ |
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{ |
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"title": " ".join(highlight_section["section_title"].strip().split()), |
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"paragraphs": "\n".join([" ".join(paragraph.strip().split()) for paragraph in highlight_section["paragraphs"]]) |
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} for highlight_section in data["highlight"] |
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] |
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if self.config.name == "plain_text": |
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yield _id, { |
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"id": _id, |
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"document": " ".join([section["title"] + " " + section["paragraphs"] if section["paragraphs"] else section["title"] for section in document_sections]).replace("\n", " ").strip(), |
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"summary": " ".join([section["paragraphs"] for section in summary_sections if section["title"] != "What GAO Recommends"]).replace("\n", " ").strip(), |
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} |
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elif self.config.name == "plain_text_with_recommendations": |
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yield _id, { |
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"id": _id, |
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"document": " ".join([section["title"] + " " + section["paragraphs"] if section["paragraphs"] else section["title"] for section in document_sections]).replace("\n", " ").strip(), |
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"summary": " ".join([section["paragraphs"] for section in summary_sections]).replace("\n", " ").strip(), |
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} |
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elif self.config.name == "structure": |
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yield _id, { |
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"id": _id, |
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"document_sections": document_sections, |
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"summary_sections": summary_sections |
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} |
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else: |
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raise ValueError("Unsupported config name {}".format(self.config.name)) |
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with open(crs_filepath, "r") as f: |
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for line in f: |
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line = line.strip() |
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if not line: |
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continue |
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data = json.loads(line) |
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_id = 'CRS_' + data["id"] |
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document_sections = _recursive_load(data["reports"], keep_letter=True, depth=0) |
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summary_sections = [{ |
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"title": "", |
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"paragraphs": "\n".join([" ".join(paragraph.strip().split()) for paragraph in data["summary"]]) |
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}] |
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if self.config.name in ["plain_text", "plain_text_with_recommendations"]: |
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yield _id, { |
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"id": _id, |
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"document": " ".join([section["title"] + " " + section["paragraphs"] if section["paragraphs"] else section["title"] for section in document_sections]).replace("\n", " ").strip(), |
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"summary": " ".join([section["paragraphs"] for section in summary_sections]).replace("\n", " ").strip(), |
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} |
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elif self.config.name == "structure": |
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yield _id, { |
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"id": _id, |
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"document_sections": document_sections, |
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"summary_sections": summary_sections |
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} |
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else: |
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raise ValueError("Unsupported config name {}".format(self.config.name)) |
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