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README.md

@@ -1,209 +0,0 @@
----
-annotations_creators:
-- no-annotation
-language_creators:
-- expert-generated
-language:
-- en
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- summarization
-task_ids: []
-pretty_name: GovReport
----
-
-
-# Dataset Card for GovReport
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Versions](#versions)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** [https://gov-report-data.github.io](https://gov-report-data.github.io)
-- **Repository:** [https://github.com/luyang-huang96/LongDocSum](https://github.com/luyang-huang96/LongDocSum)
-- **Paper:** [https://aclanthology.org/2021.naacl-main.112/](https://aclanthology.org/2021.naacl-main.112/)
-- **Leaderboard:** [Needs More Information]
-- **Point of Contact:** [Needs More Information]
-
-### Dataset Summary
-
-Government report dataset consists of reports and associated summaries written by government research agencies including Congressional Research Service and U.S. Government Accountability Office.
-
-Compared with other long document summarization datasets, government report dataset has longer summaries and documents and requires reading in more context to cover salient words to be summarized.
-
-### Versions
-
-- `1.0.1` (default): remove extra whitespace.
-- `1.0.0`: the dataset used in the original paper.
-
-To use different versions, set the `revision` argument of the `load_dataset` function.
-
-### Supported Tasks and Leaderboards
-
-[More Information Needed]
-
-### Languages
-
-English
-
-## Dataset Structure
-
-Three configs are available:
-- **plain_text** (default): the text-to-text summarization setting used as in the original paper.
-- **plain_text_with_recommendations**: the text-to-text summarization setting, with "What GAO recommends" included in the summary.
-- **structure**: data with the section structure.
-
-To use different configs, set the `name` argument of the `load_dataset` function.
-
-### Data Instances
-
-#### plain_text & plain_text_with_recommendations
-
-An example looks as follows.
-```
-{
-    "id": "GAO_123456",
-    "document": "This is a test document.",
-    "summary": "This is a test summary"
-}
-```
-
-#### structure
-
-An example looks as follows.
-```
-{
-    "id": "GAO_123456",
-    "document_sections": {
-      "title": ["test docment section 1 title", "test docment section 1.1 title"],
-      "paragraphs": ["test document\nsection 1 paragraphs", "test document\nsection 1.1 paragraphs"],
-      "depth": [1, 2]
-    },
-    "summary_sections": {
-      "title": ["test summary section 1 title", "test summary section 2 title"],
-      "paragraphs": ["test summary\nsection 1 paragraphs", "test summary\nsection 2 paragraphs"]
-    }
-}
-```
-
-### Data Fields
-
-#### plain_text & plain_text_with_recommendations
-
-- `id`: a `string` feature.
-- `document`: a `string` feature.
-- `summary`: a `string` feature.
-
-#### structure
-
-- `id`: a `string` feature.
-- `document_sections`: a dictionary feature containing lists of (each element corresponds to a section):
-  - `title`: a `string` feature.
-  - `paragraphs`: a of `string` feature, with `\n` separating different paragraphs.
-  - `depth`: a `int32` feature.
-- `summary_sections`: a dictionary feature containing lists of (each element corresponds to a section):
-  - `title`: a `string` feature.
-  - `paragraphs`: a `string` feature, with `\n` separating different paragraphs.
-
-### Data Splits
-
-- train: 17519
-- valid: 974
-- test: 973
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-Editors of the Congressional Research Service and U.S. Government Accountability Office.
-
-### Personal and Sensitive Information
-
-None.
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-CC BY 4.0
-
-### Citation Information
-
-```
-@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.",
-}
-```

data/gao_train.jsonl

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gov_report.py

@@ -1,225 +0,0 @@
-"""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))

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