SummVis

SummVis is an interactive visualization tool for analyzing abstractive summarization model outputs and datasets.

Installation

IMPORTANT: Please use python>=3.8 since some dependencies require that for installation.

git clone https://github.com/robustness-gym/summvis.git
cd summvis
pip install -r requirements.txt
python -m spacy download en_core_web_sm

Quickstart

Follow the steps below to start using SummVis immediately.

1. Download and extract data

Download our pre-cached dataset that contains predictions for state-of-the-art models such as PEGASUS and BART on 1000 examples taken from the CNN / Daily Mail validation set.

mkdir data
mkdir preprocessing
curl https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail_1000.validation.anonymized.zip --output preprocessing/cnn_dailymail_1000.validation.anonymized.zip
unzip preprocessing/cnn_dailymail_1000.validation.anonymized.zip -d preprocessing/

2. Deanonymize data

Next, we'll need to add the original examples from the CNN / Daily Mail dataset to deanonymize the data (this information is omitted for copyright reasons). The preprocessing.py script can be used for this with the --deanonymize flag.

Deanonymize 10 examples (try_it mode):

python preprocessing.py \
--deanonymize \
--dataset_rg preprocessing/cnn_dailymail_1000.validation.anonymized \
--dataset cnn_dailymail \
--version 3.0.0 \
--split validation \
--processed_dataset_path data/try:cnn_dailymail_1000.validation \
--try_it

This will take between 10 seconds and several minutes depending on whether you've previously loaded CNN/DailyMail from the Datasets library.

3. Run SummVis

Finally, we're ready to run the Streamlit app. Once the app loads, make sure it's pointing to the right File at the top of the interface.

streamlit run summvis.py

General instructions for running with pre-loaded datasets

1. Download one of the pre-loaded datasets:

CNN / Daily Mail (1000 examples from validation set): https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail_1000.validation.anonymized.zip

CNN / Daily Mail (full validation set): https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail.validation.anonymized.zip

XSum (1000 examples from validation set): https://storage.googleapis.com/sfr-summvis-data-research/xsum_1000.validation.anonymized.zip

XSum (full validation set): https://storage.googleapis.com/sfr-summvis-data-research/xsum.validation.anonymized.zip

We recommend that you choose the smallest dataset that fits your need in order to minimize download / preprocessing time.

Example: Download and unzip CNN / Daily Mail

mkdir data
mkdir preprocessing
curl https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail_1000.validation.anonymized.zip --output preprocessing/cnn_dailymail_1000.validation.anonymized.zip
unzip preprocessing/cnn_dailymail_1000.validation.anonymized.zip -d preprocessing/

2. Deanonymize n examples:

Set the --n_samples argument and name the --processed_dataset_path output file accordingly.

Example: Deanonymize 100 examples from CNN / Daily Mail:

python preprocessing.py \
--deanonymize \
--dataset_rg preprocessing/cnn_dailymail_1000.validation.anonymized \
--dataset cnn_dailymail \
--version 3.0.0 \
--split validation \
--processed_dataset_path data/100:cnn_dailymail_1000.validation \
--n_samples 100

Example: Deanonymize all pre-loaded examples from CNN / Daily Mail (1000 examples dataset):

python preprocessing.py \
--deanonymize \
--dataset_rg preprocessing/cnn_dailymail_1000.validation.anonymized \
--dataset cnn_dailymail \
--version 3.0.0 \
--split validation \
--processed_dataset_path data/full:cnn_dailymail_1000.validation \
--n_samples 1000

Example: Deanonymize all pre-loaded examples from CNN / Daily Mail (full dataset):

python preprocessing.py \
--deanonymize \
--dataset_rg preprocessing/cnn_dailymail.validation.anonymized \
--dataset cnn_dailymail \
--version 3.0.0 \
--split validation \
--processed_dataset_path data/full:cnn_dailymail.validation

Example: Deanonymize all pre-loaded examples from XSum (1000 examples dataset):

python preprocessing.py \
--deanonymize \
--dataset_rg preprocessing/xsum_1000.validation.anonymized \
--dataset xsum \
--split validation \
--processed_dataset_path data/full:xsum_1000.validation \
--n_samples 1000

3. Run SummVis

Once the app loads, make sure it's pointing to the right File at the top of the interface.

streamlit run summvis.py

Alternately, if you need to point SummVis to a folder where your data is stored.

streamlit run summvis.py -- --path your/path/to/data

Note that the additional -- is not a mistake, and is required to pass command-line arguments in streamlit.

Get your data into SummVis: end-to-end preprocessing

You can also perform preprocessing end-to-end to load any summarization dataset or model predictions into SummVis. Instructions for this are provided below.

Prior to running the following, an additional install step is required:

python -m spacy download en_core_web_lg

1. Standardize and save dataset to disk.

Loads in a dataset from HF, or any dataset that you have and stores it in a standardized format with columns for document and summary:reference.

Example: Save CNN / Daily Mail validation split to disk as a jsonl file.

python preprocessing.py \
--standardize \
--dataset cnn_dailymail \
--version 3.0.0 \
--split validation \
--save_jsonl_path preprocessing/cnn_dailymail.validation.jsonl

Example: Load custom my_dataset.jsonl, standardize, and save.

python preprocessing.py \
--standardize \
--dataset_jsonl path/to/my_dataset.jsonl \
--doc_column name_of_document_column \
--reference_column name_of_reference_summary_column \
--save_jsonl_path preprocessing/my_dataset.jsonl

2. Add predictions to the saved dataset.

Takes a saved dataset that has already been standardized and adds predictions to it from prediction jsonl files. Cached predictions for several models available here: https://storage.googleapis.com/sfr-summvis-data-research/predictions.zip

You may also generate your own predictions using this this script.

Example: Add 6 prediction files for PEGASUS and BART to the dataset.

python preprocessing.py \
--join_predictions \
--dataset_jsonl preprocessing/cnn_dailymail.validation.jsonl \
--prediction_jsonls \
predictions/bart-cnndm.cnndm.validation.results.anonymized \
predictions/bart-xsum.cnndm.validation.results.anonymized \
predictions/pegasus-cnndm.cnndm.validation.results.anonymized \
predictions/pegasus-multinews.cnndm.validation.results.anonymized \
predictions/pegasus-newsroom.cnndm.validation.results.anonymized \
predictions/pegasus-xsum.cnndm.validation.results.anonymized \
--save_jsonl_path preprocessing/cnn_dailymail.validation.jsonl

3. Run the preprocessing workflow and save the dataset.

Takes a saved dataset that has been standardized, and predictions already added. Applies all the preprocessing steps to it (running spaCy, lexical and semantic aligners), and stores the processed dataset back to disk.

Example: Autorun with default settings on a few examples to try it.

python preprocessing.py \
--workflow \
--dataset_jsonl preprocessing/cnn_dailymail.validation.jsonl \
--processed_dataset_path data/cnn_dailymail.validation \
--try_it

Example: Autorun with default settings on all examples.

python preprocessing.py \
--workflow \
--dataset_jsonl preprocessing/cnn_dailymail.validation.jsonl \
--processed_dataset_path data/cnn_dailymail

Citation

When referencing this repository, please cite this paper:

@misc{vig2021summvis,
      title={SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization}, 
      author={Jesse Vig and Wojciech Kryscinski and Karan Goel and Nazneen Fatema Rajani},
      year={2021},
      eprint={2104.07605},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2104.07605}
}

Acknowledgements

We thank Michael Correll for his valuable feedback.