
# 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.
task_categories:other annotations_creators:machine-generated language_creators:found multilinguality:monolingual size_categories:100K<n<1M source_datasets:original language:en license:unknown sentence-compression region:us
{"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}}
TAGS
#task_categories-other #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-unknown #sentence-compression #region-us 


# Dataset Card for Google Sentence Compression

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- 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



### 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



## Dataset Creation

### Curation Rationale



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information







### Contributions

Thanks to @mattbui for adding this dataset.
# Dataset Card for Google Sentence Compression ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- 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 ### 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 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

Thanks to @mattbui for adding this dataset.
TAGS
#task_categories-other #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-unknown #sentence-compression #region-us 
 # Dataset Card for Google Sentence Compression ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- 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 ### 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 ## Dataset Creation ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions

Thanks to @mattbui for adding this dataset.
passage: TAGS
#task_categories-other #annotations_creators-machine-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-unknown #sentence-compression #region-us 
# Dataset Card for Google Sentence Compression## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- 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### Languages

English## Dataset Structure passage: ### 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.