Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
machine-generated
Source Datasets:
original
License:
| annotations_creators: | |
| - machine-generated | |
| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: | |
| - unknown | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 100K<n<1M | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - other | |
| task_ids: [] | |
| paperswithcode_id: sentence-compression | |
| pretty_name: Google Sentence Compression | |
| tags: | |
| - sentence-compression | |
| dataset_info: | |
| features: | |
| - name: graph | |
| struct: | |
| - name: id | |
| dtype: string | |
| - name: sentence | |
| dtype: string | |
| - name: node | |
| sequence: | |
| - name: form | |
| dtype: string | |
| - name: type | |
| dtype: string | |
| - name: mid | |
| dtype: string | |
| - name: word | |
| sequence: | |
| - name: id | |
| dtype: int32 | |
| - name: form | |
| dtype: string | |
| - name: stem | |
| dtype: string | |
| - name: tag | |
| dtype: string | |
| - name: gender | |
| dtype: int32 | |
| - name: head_word_index | |
| dtype: int32 | |
| - name: edge | |
| sequence: | |
| - name: parent_id | |
| dtype: int32 | |
| - name: child_id | |
| dtype: int32 | |
| - name: label | |
| dtype: string | |
| - name: entity_mention | |
| sequence: | |
| - name: start | |
| dtype: int32 | |
| - name: end | |
| dtype: int32 | |
| - name: head | |
| dtype: int32 | |
| - name: name | |
| dtype: string | |
| - name: type | |
| dtype: string | |
| - name: mid | |
| dtype: string | |
| - name: is_proper_name_entity | |
| dtype: bool | |
| - name: gender | |
| dtype: int32 | |
| - name: compression | |
| struct: | |
| - name: text | |
| dtype: string | |
| - name: edge | |
| sequence: | |
| - name: parent_id | |
| dtype: int32 | |
| - name: child_id | |
| dtype: int32 | |
| - name: headline | |
| dtype: string | |
| - name: compression_ratio | |
| dtype: float32 | |
| - name: doc_id | |
| dtype: string | |
| - name: source_tree | |
| struct: | |
| - name: id | |
| dtype: string | |
| - name: sentence | |
| dtype: string | |
| - name: node | |
| sequence: | |
| - name: form | |
| dtype: string | |
| - name: type | |
| dtype: string | |
| - name: mid | |
| dtype: string | |
| - name: word | |
| sequence: | |
| - name: id | |
| dtype: int32 | |
| - name: form | |
| dtype: string | |
| - name: stem | |
| dtype: string | |
| - name: tag | |
| dtype: string | |
| - name: gender | |
| dtype: int32 | |
| - name: head_word_index | |
| dtype: int32 | |
| - name: edge | |
| sequence: | |
| - name: parent_id | |
| dtype: int32 | |
| - name: child_id | |
| dtype: int32 | |
| - name: label | |
| dtype: string | |
| - name: entity_mention | |
| sequence: | |
| - name: start | |
| dtype: int32 | |
| - name: end | |
| dtype: int32 | |
| - name: head | |
| dtype: int32 | |
| - name: name | |
| dtype: string | |
| - name: type | |
| dtype: string | |
| - name: mid | |
| dtype: string | |
| - name: is_proper_name_entity | |
| dtype: bool | |
| - name: gender | |
| dtype: int32 | |
| - name: compression_untransformed | |
| struct: | |
| - name: text | |
| dtype: string | |
| - name: edge | |
| sequence: | |
| - name: parent_id | |
| dtype: int32 | |
| - name: child_id | |
| dtype: int32 | |
| splits: | |
| - name: validation | |
| num_bytes: 55823979 | |
| num_examples: 10000 | |
| - name: train | |
| num_bytes: 1135684803 | |
| num_examples: 200000 | |
| download_size: 259652560 | |
| dataset_size: 1191508782 | |
| # Dataset Card for Google Sentence Compression | |
| ## 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://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression) | |
| - **Repository:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression) | |
| - **Paper:** [https://www.aclweb.org/anthology/D13-1155/](https://www.aclweb.org/anthology/D13-1155/) | |
| - **Leaderboard:** | |
| - **Point of Contact:** | |
| ### Dataset Summary | |
| A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automatically build a compression corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In our corpus, the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence supervised systems which require a structural alignment between the input and output can be successfully trained. We also extend an existing unsupervised compression method with a learning module. The new system uses structured prediction to learn from lexical, syntactic and other features. An evaluation with human raters shows that the presented data harvesting method indeed produces a parallel corpus of high quality. Also, the supervised system trained on this corpus gets high scores both from human raters and in an automatic evaluation setting, significantly outperforming a strong baseline. | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed] | |
| ### Languages | |
| English | |
| ## Dataset Structure | |
| ### Data Instances | |
| Each data instance should contains the information about the original sentence in `instance["graph"]["sentence"]` as well as the compressed sentence in `instance["compression"]["text"]`. As this dataset was created by pruning dependency connections, the author also includes the dependency tree and transformed graph of the original sentence and compressed sentence. | |
| ### Data Fields | |
| Each instance should contains these information: | |
| - `graph` (`Dict`): the transformation graph/tree for extracting compression (a modified version of a dependency tree). | |
| - This will have features similar to a dependency tree (listed bellow) | |
| - `compression` (`Dict`) | |
| - `text` (`str`) | |
| - `edge` (`List`) | |
| - `headline` (`str`): the headline of the original news page. | |
| - `compression_ratio` (`float`): the ratio between compressed sentence vs original sentence. | |
| - `doc_id` (`str`): url of the original news page. | |
| - `source_tree` (`Dict`): the original dependency tree (features listed bellow). | |
| - `compression_untransformed` (`Dict`) | |
| - `text` (`str`) | |
| - `edge` (`List`) | |
| Dependency tree features: | |
| - `id` (`str`) | |
| - `sentence` (`str`) | |
| - `node` (`List`): list of nodes, each node represent a word/word phrase in the tree. | |
| - `form` (`string`) | |
| - `type` (`string`): the enity type of a node. Defaults to `""` if it's not an entity. | |
| - `mid` (`string`) | |
| - `word` (`List`): list of words the node contains. | |
| - `id` (`int`) | |
| - `form` (`str`): the word from the sentence. | |
| - `stem` (`str`): the stemmed/lemmatized version of the word. | |
| - `tag` (`str`): dependency tag of the word. | |
| - `gender` (`int`) | |
| - `head_word_index` (`int`) | |
| - `edge`: list of the dependency connections between words. | |
| - `parent_id` (`int`) | |
| - `child_id` (`int`) | |
| - `label` (`str`) | |
| - `entity_mention` list of the entities in the sentence. | |
| - `start` (`int`) | |
| - `end` (`int`) | |
| - `head` (`str`) | |
| - `name` (`str`) | |
| - `type` (`str`) | |
| - `mid` (`str`) | |
| - `is_proper_name_entity` (`bool`) | |
| - `gender` (`int`) | |
| ### Data Splits | |
| [More Information Needed] | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed] | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| [More Information Needed] | |
| #### Who are the annotators? | |
| [More Information Needed] | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## 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 | |
| [More Information Needed] | |
| ### Citation Information | |
| [More Information Needed] | |
| ### Contributions | |
| Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. |