albertvillanova HF staff commited on
Commit
6958faa
·
1 Parent(s): 8f3028a

Add extractive data files

Browse files
README.md CHANGED
@@ -59,16 +59,16 @@ dataset_info:
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  dtype: string
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  splits:
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  - name: train
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- num_bytes: 6434909
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  num_examples: 6253
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  - name: test
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- num_bytes: 843181
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  num_examples: 811
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  - name: validation
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- num_bytes: 689109
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  num_examples: 661
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- download_size: 7755161
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- dataset_size: 7967199
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  configs:
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  - config_name: abstractive
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  data_files:
@@ -78,6 +78,14 @@ configs:
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  path: abstractive/test-*
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  - split: validation
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  path: abstractive/validation-*
 
 
 
 
 
 
 
 
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  ---
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  # Dataset Card for AQuaMuSe
 
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  dtype: string
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  splits:
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  - name: train
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+ num_bytes: 6434893
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  num_examples: 6253
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  - name: test
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+ num_bytes: 843165
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  num_examples: 811
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  - name: validation
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+ num_bytes: 689093
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  num_examples: 661
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+ download_size: 5162151
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+ dataset_size: 7967151
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  configs:
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  - config_name: abstractive
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  data_files:
 
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  path: abstractive/test-*
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  - split: validation
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  path: abstractive/validation-*
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+ - config_name: extractive
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+ data_files:
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+ - split: train
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+ path: extractive/train-*
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+ - split: test
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+ path: extractive/test-*
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+ - split: validation
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+ path: extractive/validation-*
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  ---
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  # Dataset Card for AQuaMuSe
dataset_infos.json CHANGED
@@ -56,38 +56,31 @@
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  },
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  "extractive": {
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  "description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)",
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- "citation": "@misc{kulkarni2020aquamuse,title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization}, author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie}, year={2020}, eprint={2010.12694}, archivePrefix={arXiv}, primaryClass={cs.CL}}",
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  "homepage": "https://github.com/google-research-datasets/aquamuse",
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  "license": "",
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  "features": {
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  "query": {
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  "dtype": "string",
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- "id": null,
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  "_type": "Value"
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  },
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  "input_urls": {
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  "feature": {
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  "dtype": "string",
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- "id": null,
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  "_type": "Value"
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  },
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- "length": -1,
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- "id": null,
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  "_type": "Sequence"
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  },
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  "target": {
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  "dtype": "string",
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- "id": null,
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  "_type": "Value"
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  }
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  },
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- "post_processed": null,
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- "supervised_keys": null,
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- "builder_name": "aquamuse",
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  "config_name": "extractive",
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  "version": {
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  "version_str": "2.3.0",
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- "description": null,
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  "major": 2,
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  "minor": 3,
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  "patch": 0
@@ -95,32 +88,25 @@
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  "splits": {
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  "train": {
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  "name": "train",
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- "num_bytes": 6434909,
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  "num_examples": 6253,
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- "dataset_name": "aquamuse"
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  },
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  "test": {
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  "name": "test",
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- "num_bytes": 843181,
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  "num_examples": 811,
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- "dataset_name": "aquamuse"
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  },
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  "validation": {
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  "name": "validation",
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- "num_bytes": 689109,
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  "num_examples": 661,
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- "dataset_name": "aquamuse"
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- }
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- },
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- "download_checksums": {
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- "https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip": {
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- "num_bytes": 7755161,
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- "checksum": "f2b4d9523031a986e545a7c0fdc8180670519696340d09179a39514fc76466d0"
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  }
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  },
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- "download_size": 7755161,
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- "post_processing_size": null,
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- "dataset_size": 7967199,
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- "size_in_bytes": 15722360
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  }
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  }
 
56
  },
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  "extractive": {
58
  "description": "AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)",
59
+ "citation": "@misc{kulkarni2020aquamuse,\n title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},\n author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},\n year={2020},\n eprint={2010.12694},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n",
60
  "homepage": "https://github.com/google-research-datasets/aquamuse",
61
  "license": "",
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  "features": {
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  "query": {
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  "dtype": "string",
 
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  "_type": "Value"
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  },
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  "input_urls": {
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  "feature": {
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  "dtype": "string",
 
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  "_type": "Value"
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  },
 
 
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  "_type": "Sequence"
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  },
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  "target": {
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  "dtype": "string",
 
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  "_type": "Value"
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  }
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  },
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+ "builder_name": "parquet",
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+ "dataset_name": "aquamuse",
 
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  "config_name": "extractive",
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  "version": {
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  "version_str": "2.3.0",
 
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  "major": 2,
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  "minor": 3,
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  "patch": 0
 
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  "splits": {
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  "train": {
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  "name": "train",
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+ "num_bytes": 6434893,
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  "num_examples": 6253,
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+ "dataset_name": null
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  },
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  "test": {
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  "name": "test",
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+ "num_bytes": 843165,
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  "num_examples": 811,
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+ "dataset_name": null
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  },
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  "validation": {
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  "name": "validation",
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+ "num_bytes": 689093,
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  "num_examples": 661,
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+ "dataset_name": null
 
 
 
 
 
 
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  }
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  },
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+ "download_size": 5162151,
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+ "dataset_size": 7967151,
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+ "size_in_bytes": 13129302
 
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  }
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  }
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