EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures

Related tags

Deep LearningSciCap
Overview

SCICAP: Scientific Figures Dataset

This is the Github repo of the EMNLP 2021 Findings' paper, SCICAP: Generating Captions for Scientific Figures (Hsu et. al, 2021)

SCICAP a large-scale figure caption dataset based on Computer Science arXiv papers published between 2010 and 2020. SCICAP contained 410k figures that focused on one of the dominent figure type - graphplot, extracted from over 290,000 papers.

How to Cite?

@inproceedings{hsu2021scicap,
  title={SciCap: Generating Captions for Scientific Figures},
  author={Hsu, Ting-Yao E. and Giles, C. Lee and Huang, Ting-Hao K.},
  booktitle={Findings of 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021 Findings)},
  year={2021}
}

Download the Dataset

You can dowload the SCICAP dataset here: Download Link (18.15 GB)

Folder Structure

scicap_data.zip
├── SciCap-Caption-All                  #caption text for all figures
│	├── Train
│	├── Val
│	└── Test
├── SciCap-No-Subfig-Img                #image files for the figures without subfigures
│	├── Train
│	├── Val
│	└── Test
├── SciCap-Yes-Subfig-Img               #image files for the figures with subfigures
│	├── Train
│	├── Val
│	└── Test
├── arxiv-metadata-oai-snapshot.json    #arXiv paper's metadata (from arXiv dataset)
└── List-of-Files-for-Each-Experiments  #list of figure names used in each experiment 
    ├── Single-Sentence-Caption
    │   ├── No-Subfig
    │   │   ├── Train
    │	│   ├── Val
    │	│   └── Test
    │	└── Yes-Subfig
    │       ├── Train
    │       ├── Val
    │       └── Test
    ├── First-Sentence                  #Same as in Single-Sentence-Caption
    └── Caption-No-More-Than-100-Tokens #Same as in Single-Sentence-Caption

Number of Figures in Each Subset

Data Collection Does the figure have subfigures? Train Validate Test
First Sentence Yes 226,608 28,326 28,327
First Sentence No 106,834 13,354 13,355
Single-Sent Caption Yes 123,698 15,469 15,531
Single-Sent Caption No 75,494 9,242 9,459
Caption w/ <=100 words Yes 216,392 27,072 27,036
Caption w/ <=100 words No 105,687 13,215 13,226

JSON Data Format

Example Data Instance (Caption and Figure)

An actual JSON object from SCICAP:

{
  "contains-subfigure": true, 
  "Img-text": ["(b)", "s]", "[m", "fs", "et", "e", "of", "T", "im", "Attack", "duration", "[s]", "350", "300", "250", "200", "150", "100", "50", "0", "50", "100", "150", "200", "250", "300", "0", "(a)", "]", "[", "m", "fs", "et", "e", "of", "ta", "nc", "D", "is", "Attack", "duration", "[s]", "10000", "9000", "8000", "7000", "6000", "5000", "4000", "3000", "2000", "1000", "0", "50", "100", "150", "200", "250", "300", "0"], 
  "paper-ID": "1001.0025v1", 
  "figure-ID": "1001.0025v1-Figure2-1.png", 
  "figure-type": "Graph Plot", 
  "0-originally-extracted": "Figure 2: Impact of the replay attack, as a function of the spoofing attack duration. (a) Location offset or error: Distance between the attack-induced and the actual victim receiver position. (b) Time offset or error: Time difference between the attack-induced clock value and the actual time.", 
  "1-lowercase-and-token-and-remove-figure-index": {
    "caption": "impact of the replay attack , as a function of the spoofing attack duration . ( a ) location offset or error : distance between the attack-induced and the actual victim receiver position . ( b ) time offset or error : time difference between the attack-induced clock value and the actual time .", 
    "sentence": ["impact of the replay attack , as a function of the spoofing attack duration .", "( a ) location offset or error : distance between the attack-induced and the actual victim receiver position .", "( b ) time offset or error : time difference between the attack-induced clock value and the actual time ."], 
    "token": ["impact", "of", "the", "replay", "attack", ",", "as", "a", "function", "of", "the", "spoofing", "attack", "duration", ".", "(", "a", ")", "location", "offset", "or", "error", ":", "distance", "between", "the", "attack-induced", "and", "the", "actual", "victim", "receiver", "position", ".", "(", "b", ")", "time", "offset", "or", "error", ":", "time", "difference", "between", "the", "attack-induced", "clock", "value", "and", "the", "actual", "time", "."]
  }, 
  "2-normalized": {
    "2-1-basic-num": {
      "caption": "impact of the replay attack , as a function of the spoofing attack duration . ( a ) location offset or error : distance between the attack-induced and the actual victim receiver position . ( b ) time offset or error : time difference between the attack-induced clock value and the actual time .", 
      "sentence": ["impact of the replay attack , as a function of the spoofing attack duration .", "( a ) location offset or error : distance between the attack-induced and the actual victim receiver position .", "( b ) time offset or error : time difference between the attack-induced clock value and the actual time ."], 
      "token": ["impact", "of", "the", "replay", "attack", ",", "as", "a", "function", "of", "the", "spoofing", "attack", "duration", ".", "(", "a", ")", "location", "offset", "or", "error", ":", "distance", "between", "the", "attack-induced", "and", "the", "actual", "victim", "receiver", "position", ".", "(", "b", ")", "time", "offset", "or", "error", ":", "time", "difference", "between", "the", "attack-induced", "clock", "value", "and", "the", "actual", "time", "."]
      }, 
    "2-2-advanced-euqation-bracket": {
      "caption": "impact of the replay attack , as a function of the spoofing attack duration . BRACKET-TK location offset or error : distance between the attack-induced and the actual victim receiver position . BRACKET-TK time offset or error : time difference between the attack-induced clock value and the actual time .", 
      "sentence": ["impact of the replay attack , as a function of the spoofing attack duration .", "BRACKET-TK location offset or error : distance between the attack-induced and the actual victim receiver position .", "BRACKET-TK time offset or error : time difference between the attack-induced clock value and the actual time ."], 
      "tokens": ["impact", "of", "the", "replay", "attack", ",", "as", "a", "function", "of", "the", "spoofing", "attack", "duration", ".", "BRACKET-TK", "location", "offset", "or", "error", ":", "distance", "between", "the", "attack-induced", "and", "the", "actual", "victim", "receiver", "position", ".", "BRACKET-TK", "time", "offset", "or", "error", ":", "time", "difference", "between", "the", "attack-induced", "clock", "value", "and", "the", "actual", "time", "."]
      }
    }
  }


Corresponding Figure: 1001.0025v1-Figure2-1.png

JSON Scheme

  • contains-subfigure: boolean (check if contain subfigure)
  • paper-ID: the unique paper ID in the arXiv dataset
  • figure-ID: the extracted figure ID of paper (the index is not the same as the label in the caption)
  • figure-type: the figure type
  • 0-originally-extracted: original captions of figures
  • 1-lowercase-and-token-and-remove-figure-index: Removed figure index and the captions in lowercase
  • 2-normalized:
    • 2-1-basic-num: caption after replacing the number
    • 2-2-advanced-euqation-bracket: caption after replacing the equations and contents in the bracket
  • Img-text: texts extracted from the figure, such as the texts for labels, legends ... etc.

Within the caption content, we have three attributes:

  • caption: caption after each normalization
  • sentence: a list of segmented sentences
  • token: a list of tokenized words

Normalized Token

In the paper, we used [NUM], [BRACKET], [EQUATION], but we decided to use NUM-TK, BRACKET-TK, EQUAT-TK in the final data release to avoid the extra problems caused by "[]".

Token Description
NUM-TK Numbers (e.g., 0, -0.2, 3.44%, 1,000,000).
BRACKET-TK Text spans enclosed by any types of bracket pairs, including {}, [], and ().
EQUAT-TK Math equations identified using regular expressions.

Baseline Performance

To examine the feasibility and challenges of creating an image-captioning model for scientific figures, we established several baselines and tested them using SCICAP. The caption quality was measured by BLEU-4, using the test set of the corresponding data collection as a reference. We trained the models on each data collection with varying levels of data filtering and text normalization. Table 2 shows the results. We also designed three variations of the baseline models, Vision-only, Vision+Text, and Text-only. Table 3 shows the results.
























Data License

The arXiv dataset uses the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license, which grants permission to remix, remake, annotate, and publish the data.

Acknowledgements

We thank Chieh-Yang Huang, Hua Shen, and Chacha Chen for helping with the data annotation. We thank Chieh-Yang Huang for the feedback and strong technical support. We also thank the anonymous reviewers for their constructive feedback. This research was partially supported by the Seed Grant (2020) from the College of Information Sciences and Technology (IST), Pennsylvania State University.

Owner
Edward
PHD Student
Edward
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Mengyang Pu 75 Dec 15, 2022
Multilingual Image Captioning

Multilingual Image Captioning Authors: Bhavitvya Malik, Gunjan Chhablani Demo Link: https://huggingface.co/spaces/flax-community/multilingual-image-ca

Gunjan Chhablani 32 Nov 25, 2022
Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

Point-Unet This is an unofficial implementation of the MICCAI 2021 paper Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segment

Namt0d 9 Dec 07, 2022
Official implementation of the ICCV 2021 paper "Joint Inductive and Transductive Learning for Video Object Segmentation"

JOINT This is the official implementation of Joint Inductive and Transductive learning for Video Object Segmentation, to appear in ICCV 2021. @inproce

Yunyao 35 Oct 16, 2022
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions

torch-imle Concise and self-contained PyTorch library implementing the I-MLE gradient estimator proposed in our NeurIPS 2021 paper Implicit MLE: Backp

UCL Natural Language Processing 249 Jan 03, 2023
This is an official implementation for "Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation".

Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation This repo is the official implementation of Exploiting Temporal Con

Vegetabird 241 Jan 07, 2023
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
Release of the ConditionalQA dataset

ConditionalQA Datasets accompanying the paper ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers. Disclaimer This dataset

14 Oct 17, 2022
[ACL 20] Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction

REval Table of Contents Introduction Overview Requirements Installation Probing Usage Citation License 🎓 Introduction REval is a simple framework for

13 Jan 06, 2023
Official public repository of paper "Intention Adaptive Graph Neural Network for Category-Aware Session-Based Recommendation"

Intention Adaptive Graph Neural Network (IAGNN) This is the official repository of paper Intention Adaptive Graph Neural Network for Category-Aware Se

9 Nov 22, 2022
YOLOV4运行在嵌入式设备上

在嵌入式设备上实现YOLO V4 tiny 在嵌入式设备上实现YOLO V4 tiny 目录结构 目录结构 |-- YOLO V4 tiny |-- .gitignore |-- LICENSE |-- README.md |-- test.txt |-- t

Liu-Wei 6 Sep 09, 2021
🇰🇷 Text to Image in Korean

KoDALLE Utilizing pretrained language model’s token embedding layer and position embedding layer as DALLE’s text encoder. Background Training DALLE mo

HappyFace 74 Sep 22, 2022
Code for Massive-scale Decoding for Text Generation using Lattices

Massive-scale Decoding for Text Generation using Lattices Jiacheng Xu, Greg Durrett TL;DR: a new search algorithm to construct lattices encoding many

Jiacheng Xu 37 Dec 18, 2022
Exploring Classification Equilibrium in Long-Tailed Object Detection, ICCV2021

Exploring Classification Equilibrium in Long-Tailed Object Detection (LOCE, ICCV 2021) Paper Introduction The conventional detectors tend to make imba

52 Nov 21, 2022
Automatic differentiation with weighted finite-state transducers.

GTN: Automatic Differentiation with WFSTs Quickstart | Installation | Documentation What is GTN? GTN is a framework for automatic differentiation with

100 Dec 29, 2022
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

CoGAIL Table of Content Overview Installation Dataset Training Evaluation Trained Checkpoints Acknowledgement Citations License Overview This reposito

Jeremy Wang 29 Dec 24, 2022
Code base for NeurIPS 2021 publication titled Kernel Functional Optimisation (KFO)

KernelFunctionalOptimisation Code base for NeurIPS 2021 publication titled Kernel Functional Optimisation (KFO) We have conducted all our experiments

2 Jun 29, 2022
Attentive Implicit Representation Networks (AIR-Nets)

Attentive Implicit Representation Networks (AIR-Nets) Preprint | Supplementary | Accepted at the International Conference on 3D Vision (3DV) teaser.mo

29 Dec 07, 2022
EMNLP 2021 Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections

Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections Ruiqi Zhong, Kristy Lee*, Zheng Zhang*, Dan Klein EMN

Ruiqi Zhong 42 Nov 03, 2022
A community run, 5-day PyTorch Deep Learning Bootcamp

Deep Learning Winter School, November 2107. Tel Aviv Deep Learning Bootcamp : http://deep-ml.com. About Tel-Aviv Deep Learning Bootcamp is an intensiv

Shlomo Kashani. 1.3k Sep 04, 2021