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JudeLee19 / HMNet-End-to-End-Abstractive-Summarization-for-Meetings

"End-to-End Abstractive Summarization for Meetings" paper - Unofficial PyTorch Implementation
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End-to-End Abstractive Summarization for Meetings (HMNet)

Implements the model described in the following paper End-to-End Abstractive Summarization for Meetings.

@article{zhu2020end,
  title={End-to-End Abstractive Summarization for Meetings},
  author={Zhu, Chenguang and Xu, Ruochen and Zeng, Michael and Huang, Xuedong},
  journal={arXiv preprint arXiv:2004.02016},
  year={2020}
}

Differences from Paper Implementation

1. I used CNN in Transformer-PositionwiseFeedForward.
2. Role vectors, pos tags, and named entity tags are not used. 
(When using a role vector, performance was lower than not utilizing role vector. So please do not hesitate to advise me about this.)

Train

python main.py --mode train --save_path path_to_save_the_model

Evaluation

python main.py --mode eval --model_path trained_model_path --gen_max_length 500
Epoch Rouge-1 Rouge-2 Rouge-L
30 0.4762 0.1862 0.1767
40 0.4796 0.1935 0.1858

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