"""GovReport: The Government Report Long Document Summarization Dataset.""" import json import datasets logger = datasets.logging.get_logger(__name__) _CITATION = """\ @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.", } """ _DESCRIPTION = """\ GovReport long document summarization dataset. There are three configs: - plain_text: plain text document-to-summary pairs - plain_text_with_recommendations: plain text doucment-summary pairs, with "What GAO recommends" included in the summary - structure: data with section structure """ _URL = "https://huggingface.co/datasets/launch/gov_report/resolve/main/data/" _URLS = { "gao_train": _URL + "gao_train.jsonl", "gao_valid": _URL + "gao_valid.jsonl", "gao_test": _URL + "gao_test.jsonl", "crs_train": _URL + "crs_train.jsonl", "crs_valid": _URL + "crs_valid.jsonl", "crs_test": _URL + "crs_test.jsonl", } def _recursive_load(section, keep_letter=False, depth=0): sections = [] if section["section_title"] != "Letter" or (section["section_title"] == "Letter" and keep_letter): sections.append({ "title": " ".join(section["section_title"].strip().split()), "paragraphs": "\n".join([" ".join(paragraph.strip().split()) for paragraph in section["paragraphs"]]), "depth": depth }) for subsection in section["subsections"]: child_sections = _recursive_load(subsection, keep_letter, depth + 1) sections.extend(child_sections) else: for subsection in section["subsections"]: child_sections = _recursive_load(subsection, keep_letter, depth) sections.extend(child_sections) return sections class GovReportConfig(datasets.BuilderConfig): """BuilderConfig for GovReport.""" def __init__(self, **kwargs): """BuilderConfig for GovReport. Args: **kwargs: keyword arguments forwarded to super. """ super(GovReportConfig, self).__init__(**kwargs) class GovReport(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.1") DEFAULT_CONFIG_NAME = "plain_text" BUILDER_CONFIGS = [ GovReportConfig( name="plain_text", version=VERSION, description="Plain text", ), GovReportConfig( name="plain_text_with_recommendations", version=VERSION, description="Plain text with GAO recommendations", ), GovReportConfig( name="structure", version=VERSION, description="structure data", ) ] def _info(self): if self.config.name in ["plain_text", "plain_text_with_recommendations"]: features = datasets.Features( { "id": datasets.Value("string"), "document": datasets.Value("string"), "summary": datasets.Value("string") } ) elif self.config.name == "structure": features = datasets.Features( { "id": datasets.Value("string"), "document_sections": datasets.features.Sequence( { "title": datasets.Value("string"), "paragraphs": datasets.Value("string"), "depth": datasets.Value("int32"), } ), "summary_sections": datasets.features.Sequence( { "title": datasets.Value("string"), "paragraphs": datasets.Value("string"), } ), } ) else: raise ValueError("Unsupported config name {}".format(self.config.name)) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage="", citation=_CITATION, ) def _split_generators(self, dl_manager): downloaded_files = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"gao_filepath": downloaded_files["gao_train"], "crs_filepath": downloaded_files["crs_train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"gao_filepath": downloaded_files["gao_valid"], "crs_filepath": downloaded_files["crs_valid"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"gao_filepath": downloaded_files["gao_test"], "crs_filepath": downloaded_files["crs_test"]}), ] def _generate_examples(self, gao_filepath, crs_filepath): """This function returns the examples in the raw (text) form.""" logger.info(f"generating examples from = (GAO) {gao_filepath} and (CRS) {crs_filepath}") with open(gao_filepath, "r") as f: for line in f: line = line.strip() if not line: continue data = json.loads(line) _id = 'GAO_' + data["id"] document_sections = [] for lv1_section in data["report"]: document_sections.extend(_recursive_load(lv1_section, keep_letter=False, depth=1)) summary_sections = [ { "title": " ".join(highlight_section["section_title"].strip().split()), "paragraphs": "\n".join([" ".join(paragraph.strip().split()) for paragraph in highlight_section["paragraphs"]]) } for highlight_section in data["highlight"] ] if self.config.name == "plain_text": yield _id, { "id": _id, "document": " ".join([section["title"] + " " + section["paragraphs"] if section["paragraphs"] else section["title"] for section in document_sections]).replace("\n", " ").strip(), "summary": " ".join([section["paragraphs"] for section in summary_sections if section["title"] != "What GAO Recommends"]).replace("\n", " ").strip(), } elif self.config.name == "plain_text_with_recommendations": yield _id, { "id": _id, "document": " ".join([section["title"] + " " + section["paragraphs"] if section["paragraphs"] else section["title"] for section in document_sections]).replace("\n", " ").strip(), "summary": " ".join([section["paragraphs"] for section in summary_sections]).replace("\n", " ").strip(), } elif self.config.name == "structure": yield _id, { "id": _id, "document_sections": document_sections, "summary_sections": summary_sections } else: raise ValueError("Unsupported config name {}".format(self.config.name)) with open(crs_filepath, "r") as f: for line in f: line = line.strip() if not line: continue data = json.loads(line) _id = 'CRS_' + data["id"] document_sections = _recursive_load(data["reports"], keep_letter=True, depth=0) summary_sections = [{ "title": "", "paragraphs": "\n".join([" ".join(paragraph.strip().split()) for paragraph in data["summary"]]) }] if self.config.name in ["plain_text", "plain_text_with_recommendations"]: yield _id, { "id": _id, "document": " ".join([section["title"] + " " + section["paragraphs"] if section["paragraphs"] else section["title"] for section in document_sections]).replace("\n", " ").strip(), "summary": " ".join([section["paragraphs"] for section in summary_sections]).replace("\n", " ").strip(), } elif self.config.name == "structure": yield _id, { "id": _id, "document_sections": document_sections, "summary_sections": summary_sections } else: raise ValueError("Unsupported config name {}".format(self.config.name))