About
SummVis is an interactive visualization tool for abstractive summarization systems, supporting analysis of models, data, and evaluation metrics.
Authors: Jesse Vig1,
Wojciech Kryściński1,
Karan Goel2,
Nazneen Fatema Rajani1
1Salesforce Research 2Stanford Hazy Research
Note: SummVis is under active development, so expect continued updates in the coming weeks and months. Feel free to raise issues for questions, suggestions, requests or bug reports.
Overview
The SummVis interface is shown below. The primary components are: (a) configuration panel, (b) source document (or reference summary, depending on configuration), (c) generated summaries (and/or reference summary, depending on configuration), (d) scroll bar with global view of annotations.
Solid underlines align n-grams between source document and the selected summary (BART). Novel words in the summary that do not appear in the source document are bolded, while novel entities are bolded in red. Stopwords are grayed out and are not used in the matching algorithms. Dotted underlines indicate tokens that are semantically related to a token in the source document. You may hover over a token to see the most semantically similar tokens in the source document (see inset image), or click on the token to auto-scroll the source document to the most similar token.
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
Installation takes around 2 minutes on a Macbook Pro.
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.
try_it
mode):
Deanonymize 10 examples (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 either a few seconds or a few 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:
https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail_1000.validation.anonymized.zip
CNN / Daily Mail (1000 examples from validation set):https://storage.googleapis.com/sfr-summvis-data-research/cnn_dailymail.validation.anonymized.zip
CNN / Daily Mail (full validation set):https://storage.googleapis.com/sfr-summvis-data-research/xsum_1000.validation.anonymized.zip
XSum (1000 examples from validation set):https://storage.googleapis.com/sfr-summvis-data-research/xsum.validation.anonymized.zip
XSum (full validation set):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
my_dataset.jsonl
, standardize, and save.
Example: Load custom 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
Loading a jsonl file directly
If you'd prefer not to run any of the pipelines above, you can load a jsonl file directly into the tool, following the format in the example file below. Note that it will take longer to load each example into the tool (~5-15 seconds on a Macbook Pro) due to computing the semantic similarity scores in realtime.
This also requires the additional install step:
python -m spacy download en_core_web_lg
Example jsonl file:
{"document": "This is the document", "summary:reference": "This is the reference summary", "summary:testmodel1": "This is the summary for testmodel1", "summary:testmodel2": "This is the summary for testmodel2"}
Simply place the file (named with .jsonl extension) in the data
directory and then select it from the File
dropdown at the top of the interface.
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.