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Datasets:
cmarkea
/
aftdb

Tasks:
Text Generation
Text-to-Image
Image-to-Text
Languages:
French
English
Tags:
arXiv
multimodal
document-type objects
License:
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metadata
license: apache-2.0
language:
  - fr
  - en
tags:
  - arXiv
  - multimodal
  - document-type objects
task_categories:
  - text-generation
  - text-to-image
  - image-to-text

We present the Arxiv Figures & Tables Database (AFTdb), which consists of an aggregation of figures and tables from scientific articles sourced from the arXiv platform.

The purpose of this dataset is to train multimodal models specialized in images of document-type objects (graphs, functional diagrams, tables, etc.), rather than photographic-type images. The idea is that a model trained on this type of data will be more coherent within the context of document corpora than a model trained on pictorial compositions. To establish a connection between the two modalities (image and text), captions for each object are also provided. As captions can sometimes be very brief, the article's summary is also included to add context to the document object if necessary. All textual data (titles, abstracts, and captions) are available in both English (original language) and French through translation using Google Translate.

For this reason, a corpus of scientific articles was prioritized. Due to the scientific rigor demanded, each document-type object is systematically accompanied by a caption (similar to captions for pictorial images on platforms like Flickr, for example).

The database is divided into two types of document objects: figures and tables. For the table part, it is possible to approach two different types of learning. The first, similar to figures, associates the image with the caption. However, in the data field, the LaTeX source code of the table is also provided. An objective can be to take an image of a table and convert it into text using this source code.

Loading the database

The figure part is relatively substantial, and it is advisable to use the dataset in streaming mode:

aftdb_figure = load_dataset("cmarkea/aftdb", "figure", streaming=True)

The table part is less substantial and can be downloaded locally directly:

aftdb_table = load_dataset("cmarkea/aftdb", "table")

Both categories are compatible, and it is possible to load both types simultaneously:

aftdb = load_dataset("cmarkea/aftdb", "figure+table", streaming=True)

This is the default configuration.

Statistical Description

The descended articles correspond to a portion of the articles that had their last modifications in the year 2023 on the arXiv platform.

Number of
articles 22,893
authors 90,165
figures (train) 157,944
figures (test) 3,579
tables (train) 16,415
tables (test) 395
total words in English titles 234,072
total words in French titles 308,187
total words in English abstracts 3,879,940
total words in French abstracts 4,536,101
total words in English captions 7,689,270
total words in French captions 8,513,199

Here is the distribution of articles in the dataset by arXiv category.

categorie Freq (%) categorie Freq (%)
cs.LG 7.29594 cs.AI 3.88624
cs.CV 2.48066 hep-ph 2.12586
astro-ph.SR 2.01854 astro-ph.GA 1.85782
stat.ME 1.77373 physics.flu-dyn 1.71847
cond-mat.stat-mech 1.66027 stat.ML 1.64265
eess.SP 1.63971 cs.CL 1.4838
astro-ph.HE 1.48087 hep-ex 1.43361
astro-ph.IM 1.43014 physics.comp-ph 1.39464
nucl-th 1.3925 math.NA 1.36794
hep-th 1.30467 physics.optics 1.28037
astro-ph.EP 1.19494 cond-mat.mtrl-sci 1.18373
cs.SY 1.17305 eess.SY 1.16131
stat.AP 1.14369 cs.IT 1.14022
math.IT 1.14022 physics.ins-det 1.1258
gr-qc 1.10845 cs.RO 1.10765
cond-mat.soft 1.05425 cond-mat.mes-hall 1.04277
astro-ph.CO 1.03743 math.OC 1.01047
cs.CR 0.994986 cond-mat.str-el 0.984041
cs.DC 0.972294 physics.chem-ph 0.95681
cond-mat.dis-nn 0.947199 cs.NI 0.941593
cond-mat.quant-gas 0.880191 physics.atom-ph 0.878322
cs.CE 0.874851 hep-lat 0.837476
cs.NE 0.836141 cs.SI 0.830001
math.DS 0.821992 eess.AS 0.813716
nucl-ex 0.810512 math-ph 0.808376
cs.HC 0.784616 cs.MM 0.709065
physics.app-ph 0.695182 cs.SD 0.694915
physics.plasm-ph 0.694381 cs.MA 0.693847
math.ST 0.682101 quant-ph 2.53645
stat.TH 0.682101 physics.bio-ph 0.650332
eess.IV 0.650065 physics.soc-ph 0.649531
cs.GR 0.633513 cs.IR 0.620965
cs.DB 0.620165 cs.CY 0.596404
cs.AR 0.576115 math.GT 0.555025
q-bio.QM 0.545948 physics.data-an 0.543812
math.CO 0.535269 math.PR 0.51845
physics.ao-ph 0.515246 nlin.CD 0.496559
stat.CO 0.49202 q-bio.PE 0.474934
cond-mat.supr-con 0.454378 q-bio.NC 0.453577
cs.GT 0.445301 econ.GN 0.429283
cs.SE 0.423143 econ.GN 0.429283
cs.ET 0.419405 physics.space-ph 0.394577
nlin.PS 0.368949 cs.PF 0.345188
physics.acc-ph 0.335845 cond-mat.other 0.331573
econ.EM 0.328903 physics.med-ph 0.320361
cs.DM 0.304876 math.AP 0.294198
nlin.AO 0.256555 q-bio.BM 0.235198
q-fin.CP 0.223184 math.AT 0.198624
cs.PL 0.192483 physics.class-ph 0.18661
math.DG 0.184741 q-fin.ST 0.181538
cs.LO 0.17433 cs.CC 0.153506
cs.DL 0.143895 q-fin.TR 0.136954
math.MG 0.135352 math.AG 0.134818
q-fin.MF 0.131615 q-bio.TO 0.126809
q-bio.GN 0.120936 math.SG 0.118266
math.GR 0.116665 math.CA 0.116398
math.CV 0.116398 cs.MS 0.110524
math.HO 0.106253 nlin.SI 0.104918
math.RT 0.100113 cs.FL 0.0995787
q-fin.PM 0.097176 econ.TH 0.0955742
math.SP 0.0880991 q-fin.GN 0.0875652
q-fin.RM 0.0859634 physics.ed-ph 0.0819589
math.QA 0.0787553 q-bio.CB 0.0752847
nlin.CG 0.072882 physics.atm-clus 0.072615
math.NT 0.0720811 math.FA 0.0712802
q-bio.MN 0.0707463 physics.pop-ph 0.064873
q-fin.PR 0.0635382 stat.OT 0.0619364
cs.OS 0.0544613 cs.SC 0.0467192
physics.gen-ph 0.0461853 physics.hist-ph 0.0429817
math.AC 0.0379093 q-bio.SC 0.0331039
math.CT 0.0309682 math.RA 0.0304342
math.GN 0.0274976 math.LO 0.0261628
cs.OH 0.0248279 math.GM 0.0168189
math.OA 0.016552 cs.GL 0.0114796
math.KT 0.00694114 q-bio.OT 0.00186877

Field Descriptions

  • id: Unique identifier for each observation.
  • paper_id: Unique arXiv identifier for each article.
  • type: 'figure' for graphic objects such as graphs, functional diagrams, etc., and 'table' for tables.
  • authors: Names of the article's authors.
  • categories: arXiv categories of the article.
  • title: Title of the article.
  • summary: Article summary.
  • caption: Caption of the document-type object.
  • image: Pillow image of the document-type object.
  • data: For figures, it represents the filename of the figure; for tables, it is the LaTeX transcription of the table.
  • newcommands: List containing the LaTeX newcommands used in the article.

Citation 

@online{DeAFTdb,
  AUTHOR = {Cyrile Delestre},
  URL = {https://huggingface.co/datasets/cmarkea/aftdb},
  YEAR = {2024},
  KEYWORDS = {NLP ; Multimodal}
}