opinosis
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The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51
topics. Topics and opinions are obtained from Tripadvisor, Edmunds.com and
Amazon.com.
| Split |
Examples |
'train' |
51 |
FeaturesDict({
'review_sents': Text(shape=(), dtype=string),
'summaries': Sequence(Text(shape=(), dtype=string)),
})
| Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
| review_sents |
Text |
|
string |
|
| summaries |
Sequence(Text) |
(None,) |
string |
|
@inproceedings{ganesan2010opinosis,
title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
pages={340--348},
year={2010},
organization={Association for Computational Linguistics}
}
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Last updated 2022-12-15 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-12-15 UTC."],[],[],null,["# opinosis\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThe Opinosis Opinion Dataset consists of sentences extracted from reviews for 51\ntopics. Topics and opinions are obtained from Tripadvisor, Edmunds.com and\nAmazon.com.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/opinosis)\n\n- **Homepage** :\n \u003chttp://kavita-ganesan.com/opinosis/\u003e\n\n- **Source code** :\n [`tfds.datasets.opinosis.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/opinosis/opinosis_dataset_builder.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): No release notes.\n- **Download size** : `739.65 KiB`\n\n- **Dataset size** : `725.45 KiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|----------|\n| `'train'` | 51 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'review_sents': Text(shape=(), dtype=string),\n 'summaries': Sequence(Text(shape=(), dtype=string)),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|--------------|----------------|---------|--------|-------------|\n| | FeaturesDict | | | |\n| review_sents | Text | | string | |\n| summaries | Sequence(Text) | (None,) | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('review_sents', 'summaries')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @inproceedings{ganesan2010opinosis,\n title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},\n author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},\n booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},\n pages={340--348},\n year={2010},\n organization={Association for Computational Linguistics}\n }"]]
