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Open Source Science

https://github.com/3mcloud/medical_conversation_summarization

Codebase for our EMNLP findings paper: https://arxiv.org/pdf/2109.12174.pdf

https://github.com/3mcloud/medical_conversation_summarization

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 1 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Codebase for our EMNLP findings paper: https://arxiv.org/pdf/2109.12174.pdf

Basic Info
  • Host: GitHub
  • Owner: 3mcloud
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 832 KB
Statistics
  • Stars: 5
  • Watchers: 3
  • Forks: 0
  • Open Issues: 9
  • Releases: 0
Created almost 4 years ago · Last pushed about 3 years ago
Metadata Files
Readme License

README.md

Leveraging BART on medical conversation summarization (3M MModal version)

This repo implements algorithm in the paper: Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations

Environment Setup

It's recommended to create your own virtual environment before setup. bash pip install -r code/requirements.txt

Repo Contents

bash code/ # source codes, see README in this folder for instructions on running the experiments data/ # should host original data files (usually .jsonl format), "dummy.jsonl" included as an example experiments/ # each experiment creates a subfolder here. "dummy/" folder shows example files that can be present

Running Experiments

See README in code/ folder for details

Citation

Please cite the paper if using this repo: bibtex @inproceedings{zhang-etal-2021-leveraging-pretrained, title = "Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations", author = "Zhang, Longxiang and Negrinho, Renato and Ghosh, Arindam and Jagannathan, Vasudevan and Hassanzadeh, Hamid Reza and Schaaf, Thomas and Gormley, Matthew R.", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.313", doi = "10.18653/v1/2021.findings-emnlp.313", pages = "3693--3712", }

Owner

  • Name: 3M
  • Login: 3mcloud
  • Kind: organization
  • Location: Maplewood, MN

Science. Applied to life.

GitHub Events

Total
  • Fork event: 1
Last Year
  • Fork event: 1

Dependencies

code/requirements.txt pypi
  • Cython ==0.29.26
  • Jinja2 ==3.0.3
  • MarkupSafe ==2.0.1
  • Pillow ==9.0.1
  • PyYAML ==6.0
  • Pygments ==2.11.2
  • QtPy ==2.0.0
  • Send2Trash ==1.8.0
  • absl-py ==1.0.0
  • antlr4-python3-runtime ==4.8
  • argon2-cffi ==21.3.0
  • argon2-cffi-bindings ==21.2.0
  • attrs ==21.4.0
  • backcall ==0.2.0
  • bert-score ==0.3.11
  • bleach ==4.1.0
  • certifi ==2021.10.8
  • cffi ==1.15.0
  • charset-normalizer ==2.0.10
  • click ==8.0.3
  • colorama ==0.4.4
  • cycler ==0.11.0
  • dataclasses ==0.6
  • debugpy ==1.5.1
  • decorator ==5.1.1
  • defusedxml ==0.7.1
  • dill ==0.3.4
  • entrypoints ==0.3
  • fairseq ==0.10.2
  • filelock ==3.4.2
  • fonttools ==4.28.5
  • huggingface-hub ==0.4.0
  • hydra-core ==1.1.1
  • idna ==3.3
  • importlib-metadata ==4.10.1
  • importlib-resources ==5.4.0
  • ipykernel ==6.7.0
  • ipython ==7.31.1
  • ipython-genutils ==0.2.0
  • ipywidgets ==7.6.5
  • jedi ==0.18.1
  • joblib ==1.1.0
  • jsonlines ==3.0.0
  • jsonschema ==4.4.0
  • jupyter ==1.0.0
  • jupyter-client ==7.1.1
  • jupyter-console ==6.4.0
  • jupyter-core ==4.9.1
  • jupyterlab-pygments ==0.1.2
  • jupyterlab-widgets ==1.0.2
  • kiwisolver ==1.3.2
  • matplotlib ==3.5.1
  • matplotlib-inline ==0.1.3
  • mistune ==0.8.4
  • nbclient ==0.5.10
  • nbconvert ==6.4.0
  • nbformat ==5.1.3
  • nest-asyncio ==1.5.4
  • nltk ==3.6.7
  • notebook ==6.4.12
  • numpy ==1.22.0
  • omegaconf ==2.1.1
  • packaging ==21.3
  • pandarallel ==1.5.4
  • pandas ==1.3.5
  • pandocfilters ==1.5.0
  • parso ==0.8.3
  • pexpect ==4.8.0
  • pickleshare ==0.7.5
  • portalocker ==2.3.2
  • prometheus-client ==0.12.0
  • prompt-toolkit ==3.0.24
  • ptyprocess ==0.7.0
  • pycparser ==2.21
  • pyparsing ==3.0.6
  • pyrsistent ==0.18.1
  • python-dateutil ==2.8.2
  • pytz ==2021.3
  • pyzmq ==22.3.0
  • qtconsole ==5.2.2
  • regex ==2021.11.10
  • requests ==2.27.1
  • rouge ==1.0.1
  • rouge-score ==0.0.4
  • sacrebleu ==2.0.0
  • sacremoses ==0.0.47
  • scikit-learn ==1.0.2
  • scipy ==1.7.3
  • sentence-transformers ==2.1.0
  • sentencepiece ==0.1.96
  • six ==1.16.0
  • tabulate ==0.8.9
  • terminado ==0.12.1
  • testpath ==0.5.0
  • threadpoolctl ==3.0.0
  • tokenizers ==0.10.3
  • torch ==1.10.1
  • torchaudio ==0.10.1
  • torchvision ==0.11.2
  • tornado ==6.1
  • tqdm ==4.62.3
  • traitlets ==5.1.1
  • transformers ==4.15.0
  • typing_extensions ==4.0.1
  • urllib3 ==1.26.8
  • wcwidth ==0.2.5
  • webencodings ==0.5.1
  • widgetsnbextension ==3.5.2
  • zipp ==3.7.0
.github/workflows/codeql-analysis.yml actions
  • actions/checkout v3 composite
  • github/codeql-action/analyze v2 composite
  • github/codeql-action/autobuild v2 composite
  • github/codeql-action/init v2 composite