Extractive-Summarizer using LexRank.

1. Import the repository folder into Pycharm

2. Download CNN datasets into project directory

   Dataset

1. Train summarizer Use training-summarizer.py on the training dataset - This application calculates the inverse document frequencies (idf) of every word in the training dataset.

   usage: training-summarizer [-h] TRAINING_DIRECTORY OUTPUT_FILE

2. Summarize test file Use test-summarizer.py on test dataset - This application summarizes a given article using previously calculated inverse document frequencies (idf).

   usage: test-summarizer [-h] IDF_FILE ORIGINAL_FILE

   note: delete .summary files if recreating summaries

3. Evaluator Use test-evaluator.py on summarized files - This application evaluates our LexRank summarizer using the sentence labels provided in the dataset.

   usage: test-evaluator [-h] SUMMARIES_DIRECTORY

1. Pull/Sync master for latest code

2. Create new feature branch from master

3. Code out the feature on the new feature branch, test locally

4. When finished, pull/sync master again

5. Update feature branch from master, resolve conflicts

6. Merge feature branch into master locally

7. Push/Sync master