Datasets:

saier
/

unarXive_citrec

	---
	annotations_creators:
	- machine-generated
	language:
	- en
	language_creators:
	- found
	license:
	- cc-by-sa-4.0
	multilinguality:
	- monolingual
	pretty_name: unarXive citation recommendation
	size_categories:
	- 1M<n<10M
	tags:
	- arXiv.org
	- arXiv
	- citation recommendation
	- citation
	- reference
	- publication
	- paper
	- preprint
	- section
	- physics
	- mathematics
	- computer science
	- cs
	task_categories:
	- text-classification
	task_ids:
	- multi-class-classification
	source_datasets:
	- extended\|10.5281/zenodo.7752615
	dataset_info:
	features:
	- name: _id
	dtype: string
	- name: text
	dtype: string
	- name: marker
	dtype: string
	- name: marker_offsets
	sequence:
	sequence: int64
	- name: label
	dtype: string
	config_name: .
	splits:
	- name: train
	num_bytes: 5457336094
	num_examples: 2043192
	- name: test
	num_bytes: 551012459
	num_examples: 225084
	- name: validation
	num_bytes: 586422261
	num_examples: 225348
	download_size: 7005370567
	dataset_size: 6594770814
	---
	# Dataset Card for unarXive citation recommendation

	## Dataset Description

	* Homepage: [https://github.com/IllDepence/unarXive](https://github.com/IllDepence/unarXive)
	* Paper: [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://arxiv.org/abs/2303.14957)

	### Dataset Summary

	The unarXive citation recommendation dataset contains 2.5 Million paragraphs from computer science papers and with an annotated citation marker. The paragraphs and citation information is derived from [unarXive](https://github.com/IllDepence/unarXive).

	Note that citation infromation is only given as the [OpenAlex](https://openalex.org/) ID of the cited paper. An important consideration for models is therefore if the data is used as is, or if additional information of the cited papers (metadata, abstracts, full-text, etc.) is used.

	The dataset can be used as follows.

	```
	from datasets import load_dataset

	citrec_data = load_dataset('saier/unarXive_citrec')
	citrec_data = citrec_data.class_encode_column('label') # assign target label column
	citrec_data = citrec_data.remove_columns('_id') # remove sample ID column
	```

	## Dataset Structure

	### Data Instances

	Each data instance contains the paragraph’s text as well as information on one of the contained citation markers, in the form of a label (cited document OpenAlex ID), citation marker, and citation marker offset. An example is shown below.

	```
	{'_id': '7c1464bb-1f0f-4b38-b1a3-85754eaf6ad1',
	'label': 'https://openalex.org/W3115081393',
	'marker': '[1]',
	'marker_offsets': [[316, 319]],
	'text': 'Data: For sentiment analysis on Hindi-English CM tweets, we used the '
	'dataset provided by the organizers of Task 9 at SemEval-2020.\n'
	'The training dataset consists of 14 thousand tweets.\n'
	'Whereas, the validation dataset as well as the test dataset contain '
	'3 thousand tweets each.\n'
	'The details of the dataset are given in [1]}.\n'
	'For this task, we did not use any external dataset.\n'}
	```

	### Data Splits

	The data is split into training, development, and testing data as follows.

	* Training: 2,043,192 instances
	* Development: 225,084 instances
	* Testing: 225,348 instances

	## Dataset Creation

	### Source Data

	The paragraph texts are extracted from the data set [unarXive](https://github.com/IllDepence/unarXive).

	#### Who are the source language producers?

	The paragraphs were written by the authors of the arXiv papers. In file `license_info.jsonl` author and text licensing information can be found for all samples, An example is shown below.

	```

	{'authors': 'Yusuke Sekikawa, Teppei Suzuki',
	'license': 'http://creativecommons.org/licenses/by/4.0/',
	'paper_arxiv_id': '2011.09852',
	'sample_ids': ['cc375518-347c-43d0-bfb2-f88564d66df8',
	'18dc073e-a48e-488e-b34c-e5fc3cb8a4ca',
	'0c2e89b3-d863-4bc2-9e11-8f6c48d867cb',
	'd85e46cf-b11d-49b6-801b-089aa2dd037d',
	'92915cea-17ab-4a98-aad2-417f6cdd53d2',
	'e88cb422-47b7-4f69-9b0b-fbddf8140d98',
	'4f5094a4-0e6e-46ae-a34d-e15ce0b9803c',
	'59003494-096f-4a7c-ad65-342b74eed561',
	'6a99b3f5-217e-4d3d-a770-693483ef8670']}
	```

	### Annotations

	Citation information in unarXive is automatically determined ([see implementation](https://github.com/IllDepence/unarXive/blob/master/src/match_references_openalex.py)).

	<!--

	## Considerations for Using the Data

	### Discussion and Biases

	TODO

	### Other Known Limitations

	TODO

	-->

	## Additional Information

	### Licensing information

	The dataset is released under the Creative Commons Attribution-ShareAlike 4.0.

	### Citation Information

	```
	@inproceedings{Saier2023unarXive,
	author = {Saier, Tarek and Krause, Johan and F\"{a}rber, Michael},
	title = {{unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network}},
	booktitle = {Proceedings of the 23rd ACM/IEEE Joint Conference on Digital Libraries},
	year = {2023},
	series = {JCDL '23}
	}
	```