# Knowledge-Augmented Figure Caption Generation

Zhishen Yang $^{1}$, Raj Dabre ${ }^{2}$, Hideki Tanaka ${ }^{2}$, Naoaki Okazaki ${ }^{1}$
School of Computing, Tokyo Institute of Technology ${ }^{1}$
National Institute of Information and Communications Technology 2
zhishen.yang@nlp.c.titech.ac.jp \{raj.dabre, hideki.tanaka\}@nict.go.jp
okazaki@c.titech.ac.jp
}

\begin{abstract}
In scholarly documents, figures are mediums for communicating scientific findings to readers in a straightforward way. Automating the generation of figure captions helps authors write informative captions that make communicating scientific findings easier to understand. In this study, figure captioning is treated as a knowledge-enhanced captioning task. To this end, we create SciCap+ by extending the large-scale SciCap dataset [1] with mention-paragraphs (paragraphs mentioning figures) and OCR tokens. Experimental results show that the mentions-paragraphs, as additional contextual knowledge, significantly improve the caption generation quality compared to the figure-only baseline. Datasets and models will be publicly released.
\end{abstract
## 1 Introduction

Figures in scientific papers provide visual representations of complex information that help to share scientific findings with readers efficiently and straightforwardly. The standard practice for scientific writing is to write a caption for each figure, accompanied by paragraphs with detailed explanations. Helping authors write appropriate and informative captions for figures will improve the quality of scientific documents, thereby facilitating scientific communication. In this study, we focus on automating the generation of captions for figures in scientific papers.

Scientific figure captioning is a variant of the image captioning task with two unique challenges: 1. Figures are not natural images: In contrast to natural images, visual objects are texts and data points in scientific figures. 2. The captions of the figures should explain: Instead of simply identifying objects and texts in the figures, the caption should contain an analysis that the authors intend to present and highlight findings.

Caption:

Fig. 7. (a) Speedup of CHEETAH over GAZELLE for computing ReLu. (b) Comparison of communication cost for ReLu.

## Mention-paragraph:

Fig. 7 plots the speedup and communication cost as a function of the output dimension. Similarly, CHEETAH achieves an outstanding speedup with much smaller communication cost, independent of the output dimension, compared with GAZELLE. .....

Figure 1 An example figure [2] with its captions and mention-paragraph and the text tokens recognized via OCR. Without referring to the mention-paragraph and the OCR tokens to tie the figure and the mention, we cannot have a proper interpretation of the data presented in the figure, which is communication cost comparison and speed up of CHEETAH over GAZELLE.

The previous study [1] defines the scientific figure captioning task as the figure-to-caption task. Their experiments report relatively lower automatic evaluation scores, indicating considerable room for improvement. Intuitively, humans need to have sufficient background knowledge to interpret figures. As figure 1 shows, only by looking at the figure, we do not know what "comm.(KB)" stands for; therefore, lacking the knowledge to write informative captions is challenging. However, the mention-paragraph contains "communication cost" and this is also present in the caption, indicating that such background knowledge should help in writing accurate captions.

Based on above observations, we hypothesized that gen-

Figure 2 The overall workflow of the data augmentation for creating the SciCap+ dataset. For each figure in the SciCap+, we extracted its mention-paragraphs and OCR tokens (OCR texts and bounding boxes).

erating appropriate captions is infeasible without adding context knowledge to the caption generation model. This context takes two forms: background knowledge of running text and OCR tokens in a figure, which should help provide additional context to the model. We then presented scientific figure captioning as a multimodal summarization task and used the M4C captioning model [3] (a model that uses multimodal knowledge to generate captions) as a starting point to investigate the scientific figure captioning task. The experimental result of automatic evaluation demonstrates that using knowledge embedded across modalities, especially in the form of mention-paragraphs and OCR tokens, significantly boosts performance.

## 2 Problem Formulation

[1] pose scientific figure captioning as an image captioning task as: Given a figure $I$, the model generates a caption $C=\left[c_{0}, c_{1}, \ldots, c_{N}\right]$. However, we reframe this task as a knowledge-augmented image captioning task that requires knowledge extracted from text and vision modalities. Given a scientific figure $I$ and knowledge extracted from text and vision modality: $K_{\text {text }}=\left(M_{t}, O_{t}\right)$, and $K_{\text {vision }}=\left(I_{v}, O_{v}, O_{p}\right)$, where $M_{t}$ and $O_{t}$ are text features extracted from paragraphs that mention figures (mentionparagraphs) and OCR texts. $I_{v}$ and $O_{v}$ are visual features obtained from figures and OCR texts. $O_{p}$ represents locations of OCR texts in figures. We define the figure caption

Table 1 Statistics of the SciCap+ dataset.

generation task as modelling the conditional probability: $P\left(C \mid I, K_{\text {text }}, K_{\text {vision }}\right)$.

## 3 SciCap+ Dataset

SciCap is a large-scale figure-caption dataset comprising graph plots extracted from 10 years of collections of arXiv computer science papers. We used around 414k figures from SciCap and augment each figure with its mentionparagraphs and OCR tokens with metadata. This section details the data set creation and data augmentation processes. Figure 2 shows the overall workflow behind the creation of the SciCap+.

Data Statistics We split figures at the document level and kept all original captions and figures (graph plots, with/without sub-figures). For a figure, we kept only the first paragraph that mentions it in the body text. Table 1 shows statistics of the SciCap+ dataset.

Mention-paragraph Extraction We first obtained papers in PDF format from Kaggle arXiv dastaset ${ }^{1)}$. The reason for using PDFs is that not all papers have source

1) https://www.kaggle.com/datasets/Cornell-University/ arxiv

Figure 3 Score distribution on correlations between mentionparagraph, OCR tokens and figure captions. Both evaluators judged most of the figures with at least moderate correlations with captions.

files and some are complicated to parse. After obtaining PDFs, we used PDFFigures $2.0[4]^{2)}$ to extract the body text of each paper. PDFFigure 2.0 is a tool that extracts figures, captions, tables, and text from scholarly PDFs in computer science. In scholarly documents, authors label figures with numbers (e.g. Figure 1. Fig. 1). For a figure, we used its figure number in a regular expression to locate a paragraph that mentions it.

OCR Extraction The SciCap dataset also provides texts extracted from figures as metadata, but does not provide location information for each text. To include location information for each text in a figure, we used Google Vision OCR API to extract text tokens from each figure with its coordinates of bounding boxes.

## 3.1 Dataset Quality Evaluation

We conducted human evaluations of the SciCap+ where we checked the mention-paragraphs and OCR tokens extraction quality. The aim was to establish whether the mention-paragraphs and OCR tokens were extracted correctly and relevant to the figure and its caption. To this end, we randomly selected 200 figures from the training set and for each figure, we asked two human evaluators to give scores of 1-5 (1 represents no relevance and 5 is highly relevant) for relevance between a caption of a figure and its mention-paragraphs and OCR tokens.

Figure 3 shows the distributions of the relevance scores. We can observe that two evaluators gave most of the figures (evaluator 1: $64 \%$ and evaluator 2: $79.5 \%$ ) with relevance scores greater than 3 and a cohen kappa score of 0.28. This evaluation result indicates that the mentionparagraphs and OCR tokens have a satisfactory extraction

2) https://github.com/allenai/pdffigures2 quality and that the annotators considered most of them as relevant to the figure and its caption. However, the two annotators seem to have a relatively lower agreement $(0.28)$ regarding which figures and captions are relevant to their mention-paragraphs and OCR tokens. We attribute this to the fact that evaluations of figure captions are highly subjective.

## 4 Experimental Results

This section reports experimental results using M4CCaptioner [3] as the baseline model to study the challenge of scientific figure captioning using the SciCap+ dataset. Please refer to the appendix for implementation and training details.

## 4.1 Main Result

The experimental results in table 2 demonstrate that using the mention-paragraph and OCR tokens significantly improves scores on all five metrics compared to the figureonly baseline. The experimental results align with our hypothesis that scientific figure captioning is a knowledgeaugmented image captioning task, OCR tokens and knowledge embedded in mention-paragraphs help in composing informative captions.

We established a baseline M4C-Captioner (Figure only) with figures as the only input modality to the M4CCaptioner model in row \#1. This baseline is in the nonknowledge setting. Therefore, low scores in all metrics show that the model needs knowledge of other modalities. Using the mention only in row \#2 shows that the mention certainly contains a lot of useful information, as evidenced by the increase in performance. When OCR features are added to the figure input in row \#3, scores for all metrics have significant gains compared to the figureonly baseline, but are still weaker than when only mentions are used. This motivates the combination of mentions and OCR features and in row \#4, compared to the figure-only baseline and figure-OCR-only baseline, the performance further improves. Perhaps the most interesting result is in row \#5 where we only use the mentions and OCR features but not the figure and get the best performance, particularly for SPICE and CIDEr, albeit comparable to when the figure is included in row \#4. All these results indicate that explicitly extracted multimodal knowledge helps to compose informative captions.

Table 2 Automatic evaluation scores of M4C-captioning on SciCap dataset. Aggregate knowledge from text and vision modalities significantly boosts the model performance compared to the figure-only baseline.

## 4.2 Ablation Studies

We first performed an ablation study on figures by removing visual feature vectors, the CIDEr score increases slightly, indicating that the visual feature is more like noise for the model. This is likely because the Resnet- 152 visual encoder we used was not trained on figures.

We enriched the representations of the OCR features by adding text, visual, and spatial features. Ablation studies aim to reveal impacts of each OCR token feature. All comparisons are with row \#4 even though row \#5 gives slightly better scores. With OCR features completely removed in row \#6, the CIDEr scores decrease by 5.3. Using only OCR spatial features in row \#7, the CIDEr score dropped by 7.8. Removing OCR spatial features in row \#8, the CIDEr scores dropped by 1.2. Upon removal of OCR visual features in row \#9, the CIDEr score is close to removing spatial features.

The above ablation study indicates that the enriched OCR contributes to the informativeness of generated captions. Unlike OCR features, where appearance features are helpful to the model, removing visual features of figures increases CIDEr scores, further indicating that we need a specific vision encoder for figures to provide meaningful features.

## 5 Related Work

Unlike natural image captioning, figure captioning has been less explored. SciCap [1] is the most recent work on scientific figure captioning, comprising a large-scale scientific figure captioning dataset that includes figures from academic papers in arXiv dataset. Before SciCap,
FigCAP [5] [6] and FigureQA [7] are two figure captioning datasets, but their figures are synthesized. We decided to extend and study on SciCap dataset, since its figures are from real-world scientific papers.

The closest multimodal task to figure captioning is image captioning. Recent works on integrating texts in natural images for visual question answering and image captioning tasks are based on transformer architecture augmented with a pointer network $[8,9]$. The transformer enriches representations by integrating knowledge from both text and visual modality. The pointer network dynamically selects words from the fixed dictionary or OCR tokens during generation.

## 6 Conclusion

In this paper, we focus on scientific figure captioning as a knowledge augmented image captioning task. We transform the SciCap dataset [1]into SciCap+, by augmenting figures with their mention-paragraphs and OCR tokens. We then benchmark SciCap+ using the M4C-Captioner as the baseline model to utilize knowledge across three modalities: mention-paragraphs, figures, and OCR tokens. The experimental results reveal that using knowledge significantly improves evaluation metric scores. The release of the SciCap+ dataset promotes the further development of scientific figure captioning. For future work, we are interested in how to use multimodal pretraining strategies in this task.

## Acknowledgment

These research results were partly obtained from the commissioned research (No. 225) by National Institute of Information and Communications Technology (NICT), Japan, and partly obtained from the first author's internship research under NICT.

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## A Appendix

## A. 1 Implementation and Training

Our implementation of M4C-Captioner is based on the MMF framework [10] and Pytorch. The implementation allows users to specify diverse pre-trained encoders for each modality, which can be fine-tuned or frozen during training. The M4C-captioner itself has $D=768$ hidden dimension size, $K=4$ transformer layers and 12 attention heads. We used sentencepiece [11] to obtain a dictionary of 32000 subwords built from both mention-paragraphs and OCR tokens. This is used as the M4C-captioner's vocabulary. We followed the BERT-BASE hyperparameter setting and trained from scratch.

Regarding the encoders that feed features to M4Ccaptioner, we used pre-trained Resnet-152 as the figure's vision encoder. For each figure, we applied a 2D adaptive average pooling over outputs from layer 5 to obtain a global visual feature vector with a dimension of 2048 . Layers 2, 3 and 4 layers were fine-tuned during training. For mention-paragraph features, SciBERT [12] was used to encode ${ }^{3)}$ it into 758-dimensional feature vectors. The number of vectors equals the number of sub-word tokens in the mention-paragraph, which we limit to 192. The mention-paragraph encoder is also fine-tuned during training. Finally, for OCR tokens, we use both text and visual features. We selected FastText [13] as the word encoder and Pyramidal Histogram of Characters (PHOC) [14] as the character encoder. Regarding the visual feature encoder of OCR tokens, we first extracted Faster R-CNN fc6 features and then applied fc 7 weights to it to obtain 2048-dimensional appearance features for bounding boxes of OCR tokens. The $\mathrm{fc} 7$ weights were fine-tuned during training. We kept a maximum of 95 OCR tokens per figure.

We trained a model on a GPU server with 8 Nvidia Tesla V100 GPUs. Training a model with a complete set of features took 13 hours. During training, we used a batch size of 128. We selected CIDEr as the evaluation metric. The evaluation interval is every 2000 iterations, we stop training if CIDEr score does not improve for 4 evaluation intervals. The optimizer is Adam with a learning rate of 0.001 and $\epsilon=1.0 \mathrm{E}-08$. We also used a multistep learning rate schedule with warmup iterations of 1000 and a warmup factor of 0.2. We kept the maximum number of decoding steps at the decoding time as 67.

For evaluation, we used five standard metrics for evaluating image captions: BLEU-4 [15], METEOR [16], ROUGE-L [17], CIDEr [18] and SPICE [19]. Since figure captions contain scientific terms which can be seen as uncommon words, among all five metrics, we are particularly interested in CIDEr since it emphasizes them.

3) We only used the first 3 layers of SciBERT for lightweightness.
 Zhishen Yang $^{1}$, Raj Dabre ${ }^{2}$, Hideki Tanaka ${ }^{2}$, Naoaki Okazaki ${ }^{1}$ School of Computing, Tokyo Institute of Technology ${ }^{1}$ National Institute of Information and Communications Technology 2 zhishen.yang@nlp.c.titech.ac.jp \{raj.dabre, hideki.tanaka\}@nict.go.jp okazaki@c.titech.ac.jp }  \begin{abstract} In scholarly documents, figures are mediums for communicating scientific findings to readers in a straightforward way. Automating the generation of figure captions helps authors write informative captions that make communicating scientific findings easier to understand. In this study, figure captioning is treated as a knowledge-enhanced captioning task. To this end, we create SciCap+ by extending the large-scale SciCap dataset [1] with mention-paragraphs (paragraphs mentioning figures) and OCR tokens. Experimental results show that the mentions-paragraphs, as additional contextual knowledge, significantly improve the caption generation quality compared to the figure-only baseline. Datasets and models will be publicly released. \end{abstract 