A module for E-mail Summarization which uses clustering of skip-thought sentence embeddings.
This code in this repository compliments this Medium article.

• The code is written in Python 2.
• The module uses code of the Skip-Thoughts paper which can be found here. Do:

  git clone https://github.com/ryankiros/skip-thoughts
  

• The code for the skip-thoughts paper uses Theano. Make sure you have Theano installed and GPU acceleration is functional for faster execution.
• Clone this repository and copy the file email_summarization.py to the root of the cloned skip-thoughts repository. Do:

  git clone https://github.com/jatana-research/email-summarization
  cp email-summarization/email_summarization.py skip-thoughts/
  

• Install dependencies. Do:

  pip install -r email-summarization/requirements.txt
  python -c 'import nltk; nltk.download("punkt")'
  

• Download the pre-trained models. The total download size will be of around 5 GB. Do:

  mkdir skip-thoughts/models
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/dictionary.txt
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/utable.npy
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/btable.npy
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/uni_skip.npz
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/uni_skip.npz.pkl
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/bi_skip.npz
  wget -P ./skip-thoughts/models http://www.cs.toronto.edu/~rkiros/models/bi_skip.npz.pkl
  

• Verify the MD5 hashes of the downloaded files to ensure that the files haven't been corrupted during the download. Do:

  md5sum skip-thoughts/models/*
  

  The output should be:

  9a15429d694a0e035f9ee1efcb1406f3 bi_skip.npz
  c9b86840e1dedb05837735d8bf94cee2 bi_skip.npz.pkl
  022b5b15f53a84c785e3153a2c383df6 btable.npy
  26d8a3e6458500013723b380a4b4b55e dictionary.txt
  8eb7c6948001740c3111d71a2fa446c1 uni_skip.npz
  e1a0ead377877ff3ea5388bb11cfe8d7 uni_skip.npz.pkl
  5871cc62fc01b79788c79c219b175617 utable.npy
  

• Change Lines:23-24 in the file skip-thoughts/skipthoughts.py to provide the correct paths to the downloaded models.

  path_to_models = 'models/'
  path_to_tables = 'models/'
  

• Find any English emails dataset online or create a small one on your own.
• The module expects a list of emails as input and returns a list of summaries.
• Open the Python interpreter in the skip-thoughts/ folder and do:

  >>> from email_summarization import summarize
  >>> summaries = summarize(emails) # emails is a Python list containing English emails.