Multilingual Summarization Evaluation (MuSE) is a tool for evaluating summarization systems across multiple languages. MuSE supports multiple domains, including single document, multi-document, and conversations.
Muse can be used as a command line tool or as a python library, examples of this can be found in the tutorial folder.
Muse supports natively the following summarization systems:
- crossSum
- falconsAI
- mT5
- spacy
- sumy
And the following evaluation metrics:
- bertScore
- bleu
- meteor
- rouge
- ollama (our own metric utilising llms via ollama)
We also provide a notebook server through the docker interface.
The following command will evaluate the sumy summarization system on the document domain using the rouge metric and the english language:
muse -s sumy -t document -d ./examples/ -m rougemetric -l en
In order to use MuSE, you will need ollama installed. You can find the instructions for installing ollama here.
You can then clone the repository with:
git clone [email protected]:hdg7/muse.git
and you can then install the requirements with:
pip install -r requirements.txt
You can also optionally install the development requirements with:
pip install -r optional-requirements.txt
To install the package, once you have cloned the repository, you can run the install script with:
./install.bash
TODO: Add instructions for installing the package from pypi.
Build the docker image with:
docker build -t muse .
Run the docker image in development mode with:
docker run --gpus all muse -p 8888:8888
Alternatively, you can use the docker compose
command to start the deployment mode with:
Or, if you have the compose plugin:
docker compose up --build
This will also link the outputs
folder to a local outputs
folder, so you can access the outputs from the jupyter notebook.