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AJ-Summarisation

A tool to generate summaries.

Installation

  1. Use a vritual environment using either virtualenv or anaconda
    • a. virtualenv:
    sudo pip3 install -U virtualenv
virtualenv --system-site-packages -p python3 ./venv
source ./venv/bin/activate  # sh, bash, ksh, or zsh
    • b. anaconda: Download anaconda or miniconda from their website
    conda create -n tfg  tensorflow-gpu  tensorflow=2
conda activate tfg
  2. Download the following packages and tools (we used pip install):
  rouge
  nltk
  tensorflow
  tensorflow_hub
  tqdm
  sklearn
  scipy
  numpy
  pickle

Data

To fully run this project you will need to individually obtain the data from Cornell NEWSROOM. After downloading and unzipping the data, make the directory ~/AJ-summarization/input_data and place the train, dev, and test files in that folder.

Usage

  1. Make sure to be in virtual environment
  2. cd to AJ-summarization
  3. python3 [summarization strategy file] (listed below)
  4. cd evaluation_src
  5. python3 evaluation.py

List of summarization strategies:

summarization-baseline.py : Takes the first sentence of each article.

summarization.py : Uses our modified TextRank to generate the summary.

first-bias.py : First bias technique

weighted.py : Statistical weighting strategy

In the future, we plan to add a bash file that can run the desired strategies and evaluation in one command.

Contributing

Accepting No Pull Requests For Now.

License

MIT