|
--- |
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annotations_creators: |
|
- machine-generated |
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language_creators: |
|
- found |
|
language: |
|
- en |
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license: |
|
- unknown |
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multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 100K<n<1M |
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source_datasets: |
|
- original |
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task_categories: |
|
- other |
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task_ids: [] |
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paperswithcode_id: sentence-compression |
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pretty_name: Google Sentence Compression |
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tags: |
|
- sentence-compression |
|
dataset_info: |
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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) |
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- **Repository:** [https://github.com/google-research-datasets/sentence-compression](https://github.com/google-research-datasets/sentence-compression) |
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- **Paper:** [https://www.aclweb.org/anthology/D13-1155/](https://www.aclweb.org/anthology/D13-1155/) |
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- **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] |
|
|
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### Languages |
|
|
|
English |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
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|
|
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. |
