| --- |
| 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.", |
| } |
| ``` |
|
|
