There are 2 labeling functions in TransformerSum (Combination vs Greedy), both are based on established paper, while the labeling function in BillSum is more naive (also based on ROUGE score). As a result, I decided to use TransformerSum's labeling functions instead.
Both Combination Selection and Greedy Selection are available for BillSum dataset now with #22.
LABELING FUNCTION: Verify the Labeling Function difference between TransformerSum (https://github.com/achen353/TransformerSum/blob/master/src/convert_to_extractive.py) and BillSum (https://github.com/FiscalNote/BillSum/blob/master/billsum/data_prep/label_sentences.py), adopt BillSum and make it an option in convert_to_extractive.py. Prepare the data with the labeling function
DUE: 11/20 Saturday 11:59pm