SQuALITY: Building a Long-Document Summarization Dataset the Hard Way
Paper • 2205.11465 • Published • 1
metadata dict | document stringlengths 18.7k 38.3k | questions listlengths 5 5 |
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Produced by Greg Weeks, Mary Meehan and the Online
Distributed Proofreading Team at http://www.pgdp.net
CULTURAL EXCHANGE
BY KEITH LAUMER
It was a simple student exchange—but
Retief gave them more of
an education than they expected!
[Transcriber's Note: This etext was produced from
Worlds of I... | [
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"question_text": "What is the plot of the story?",
"question_number": 1,
"responses": [
{
"worker_id": "2",
"uid": "18c9c61e7ecc4132b9ef185ffceda36f",
"response_text": "Second Secretary Magnan will be away from the Manpower Utilization Directorate, Division of Libraries a... |
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"license": "This eBook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Guten... | THE RECRUIT
BY BRYCE WALTON
It was dirty work, but it would make him a man. And kids had a right to grow up—some of them!
[Transcriber's Note: This etext was produced from Worlds of If Science Fiction, July 1962. Extensive research did not uncover any evidence that the U.S. copyright on this publication was ... | [
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"uid": "220b7d4245314ae1bc5be811e3aeff6a",
"response_text": "The story opens on a discussion at home between a husband and wife being overheard by their sixteen-yea... |
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Produced by Greg Weeks, Mary Meehan and the Online
Distributed Proofreading Team at http://www.pgdp.net
The Monster Maker
By RAY BRADBURY
"Get Gunther," the official orders read. It
was to laugh! For Click and Irish were
marooned on the pirate's asteroid—their only
weapons a single gun and a news-... | [
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"uid": "2395af6d550e4c6b9137b6d7a2e785d1",
"response_text": "“Click” Hathaway, a photographer, is on a spaceship with “Irish” Marnagan, the ship’s pilot, as the shi... |
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End of preview. Expand in Data Studio
YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
SQuALITY - v1.3
Original paper here
This is v1.3, the 'text' edition .jsonl files. See description from the original repo:
v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.
contents
again, this is taken from the repo
Each data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:
- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license
- document: the Gutenberg story
questions: a list of questions and accompanying responses
- question text
- question number: the order in which that question was answered by the writers
- responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question
dataset contents
DatasetDict({
train: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 50
})
test: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 52
})
validation: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 25
})
})
- Downloads last month
- 31
Size of downloaded dataset files:
7.41 MB
Size of the auto-converted Parquet files:
4.17 MB
Number of rows:
127