Chart-to-Text: Generating Natural Language Explanations for Charts by Adapting the Transformer Model
Code for Chart-to-Text: Generating Natural Language Explanations for Charts by Adapting the Transformer Model.
Much of the code is adapted from Enhanced Transformer Model for Data-to-Text Generation [PDF] (Gong, Crego, Senellart; WNGT2019). https://github.com/gongliym/data2text-transformer
This project aims to automatically generate salient summaries from a chart's data table using a modified Transformer model.
Newer paper + dataset on this topic: Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (Paper on arXiv)
Also visit SciCap: Scientific Figures Dataset for a related project with a larger and more detailed dataset.
The dataset is stored in the dataset directory. There are individual files for each statistic's title, data, and caption in their respective folders.
Statistics which only contains two columns i.e simple bar or line charts are found in the dataset folder, and statistics with more than two columns i.e grouped bar or multi-line charts are found in the dataset/multiColumn folder.
There are also uncleaned versions found in the _old folders.
Chart type breakdown:
| Line | Bar | Total: | |
|---|---|---|---|
| Simple | 3564 | 3199 | 6763 |
| Complex | 902 | 640 | 1542 |
| Total: | 4466 | 3839 |
Images available seperately at https://github.com/JasonObeid/Chart2TextImages due to large size ~1GB
Create a new conda environment and install the necessary packages
conda create --name chart2text
conda activate chart2text
cd Chart2Text
pip install -r requirements.txt
Note that en_core_web_md==2.2.5 cannot be installed with pip, instead install using the command
python -m spacy download en_core_web_sm
according to https://spacy.io/models/en
(However if you have done all the preprocessing then this en_core_web_md package can be skipped, and you can jump to the Generation section of the readme to proceed.)
Clean the text within the chart titles and summaries
python utils/refactorTitles.py
python utils/refactorCaptions.py
python etc/templatePreprocess.py
- Converts data tables into a sequence of records (taken as input by the model):
data/*split*/trainData.txt - Cleans summary tokens and substitutes any possible tokens with data variables(e.g., 2018 -> templateValue[0][0]):
data/*split*/trainSummary.txt - Cleans the title tokens:
data/*split*/trainTitle.txt - Labels the occurrences of records mentioned within the summary:
data/*split*/trainDataLabel.txt - Labels the summary tokens which match a record:
data/*split*/trainSummaryLabel.txt - Saves the gold summaries:
data/*split*/testOriginalSummary.txt
cd etc
python extract_vocab.py --table ../data/valid/validData.txt --summary ../data/valid/validSummary.txt
python extract_vocab.py --table ../data/test/testData.txt --summary ../data/test/testSummary.txt
python extract_vocab.py --table ../data/train/trainData.txt --summary ../data/train/trainSummary.txt
It will generate vocabulary files for each of them:
data/*split*/trainData.txt_vocabdata/*split*/trainSummary.txt_vocab
cd ../model
python preprocess_table_data.py --table ../data/valid/validData.txt --table_vocab ../data/valid/validData.txt_vocab --table_label ../data/valid/validDataLabel.txt
python preprocess_table_data.py --table ../data/test/testData.txt --table_vocab ../data/test/testData.txt_vocab --table_label ../data/test/testDataLabel.txt
python preprocess_table_data.py --table ../data/train/trainData.txt --table_vocab ../data/train/trainData.txt_vocab --table_label ../data/train/trainDataLabel.txt
python preprocess_summary_data.py --summary ../data/valid/validSummary.txt --summary_vocab ../data/valid/validSummary.txt_vocab --summary_label ../data/valid/validSummaryLabel.txt
python preprocess_summary_data.py --summary ../data/test/testSummary.txt --summary_vocab ../data/test/testSummary.txt_vocab --summary_label ../data/test/testSummaryLabel.txt
python preprocess_summary_data.py --summary ../data/train/trainSummary.txt --summary_vocab ../data/train/trainSummary.txt_vocab --summary_label ../data/train/trainSummaryLabel.txt
Outputs the training data:
- Data Records:
data/*split*/trainData.txt.pth - Summaries:
data/*split*/trainSummary.txt.pth
Note: if you get a dictionary assertion error, then delete the old .pth files in data subfolders and try again
MODELPATH=$PWD/model
export PYTHONPATH=$MODELPATH:$PYTHONPATH
python $MODELPATH/train.py
## main parameters
python model/train.py \
--model_path "experiments" \
--exp_name "chart2text" \
--exp_id "run1" \
--train_cs_table_path data/train/trainData.txt.pth \
--train_sm_table_path data/train/trainData.txt.pth \
--train_sm_summary_path data/train/trainSummary.txt.pth \
--valid_table_path data/valid/validData.txt.pth \
--valid_summary_path data/valid/validSummary.txt.pth \
--cs_step True \
--lambda_cs "1" \
--sm_step True \
--lambda_sm "1" \
--label_smoothing 0.05 \
--sm_step_with_cc_loss False \
--sm_step_with_cs_proba False \
--share_inout_emb True \
--share_srctgt_emb False \
--emb_dim 512 \
--enc_n_layers 1 \
--dec_n_layers 6 \
--dropout 0.1 \
--save_periodic 40 \
--batch_size 6 \
--beam_size 4 \
--epoch_size 1000 \
--max_epoch 81 \
--eval_bleu True \
--sinusoidal_embeddings True \
--encoder_positional_emb True \
--gelu_activation True \
--validation_metrics valid_mt_bleu
Use the following commands to generate from the above models:
Download our model (trained with data variables)
python model/summarize.py
--model_path aug17-80.pth
--table_path data/test/testData.txt \
--output_path results/aug17/templateOutput-p80.txt \
--title_path data/test/testTitle.txt --beam_size 4 --batch_size 8
Substitute any predicted data variables:
python etc/summaryComparison.py
python studyOutcome/automatedEvaluation.py
The BLEU evaluation script can be obtained from Moses:
perl model/src/evaluation/multi-bleu.perl data/test/testOriginalSummary.txt < results/aug17/generated-p80.txt
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