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README.md

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+---
+annotations_creators:
+- no-annotation
+language_creators:
+- expert-generated
+languages:
+- en
+licenses:
+- cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- summarization
+task_ids:
+- summarization
+pretty_name: GovReport
+---
+
+
+# Dataset Card for GovReport
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [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.
+
+### 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/crs_test.jsonl

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gov_report.py

@@ -0,0 +1,225 @@
+"""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/shuyangcao/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": section["section_title"].strip(),
+            "paragraphs": "\n".join(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("1.0.0")
+
+    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))