Implementation of the paper Get To The Point: Summarization with Pointer-Generator Networks by Abigail See
The Dataset considered is same as the one used in the paper. It can be obtained from the link.
The version 1 consists of the basic Bahdanau attention model.
This version is the baseline implementation of the model specifed in the paper. A varient of Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond by Ramesh Nallapati without the large vocab trick. The vocab has been limited to a size of 50k by taking a minimum frequency of 27 while constructing the vocab.
This version consists of the original model proposed in the paper. It is run seperately for pointer-gen and pointer-gen+coverage for experiment purposes.
Each of the repository can be independently run by the following:
- Version 1: Run train.py
- Version 2: Run train.py
- Version 3: Run train.py(with appropriate tags for pointer gen and coverage)
P.S: The data after preprocessing and converted to pkl object(list of torchtext examples) is req to be present in data folder in the home directory in order to run the above