Awesome

HMNet

This is the official code for the Microsoft's paper of HMNet model at EMNLP 2020. It is implemented under PyTorch framework. The related paper to cite is:

@Article{zhu2020a,
author = {Zhu, Chenguang and Xu, Ruochen and Zeng, Michael and Huang, Xuedong},
title = {A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining},
year = {2020},
month = {November},
url = {https://www.microsoft.com/en-us/research/publication/end-to-end-abstractive-summarization-for-meetings/},
journal = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
}

Finetune HMNet

It is recommended to run our model inside a docker:

Build docker image

cd Docker
sudo docker build . -t hmnet

Run container from image

sudo nvidia-docker run -it hmnet /bin/bash

Get the pretrained HMNet ready at ExampleInitModel/HMNet-pretrained. Please see document.

Finetune on AMI dataset

CUDA_VISIBLE_DEVICES="0,1,2,3" mpirun -np 4 --allow-run-as-root python PyLearn.py train ExampleConf/conf_hmnet_AMI

The training log/model/settings could be found at ExampleConf/conf_hmnet_AMI_conf~/run_1

Data paths

ExampleRawData/meeting_summarization/AMI_proprec: The preprocessed AMI dataset. The *.json files point to the path to each split. Each folder (train, dev or test) contains the compressed chunks of data in the format for infinibatch.
ExampleRawData/meeting_summarization/ICSI_proprec: Same as above for ICSI dataset.
ExampleInitModel/transfo-xl-wt103: Here we only used the vocabulary from Transformer-XL, provided by Huggingface.

Evaluation

Step 1: specify the model path

In ExampleConf/conf_eval_hmnet_AMI, for the line

PYLEARN_MODEL ###

Replace ### to the real checkpoint path. Use the relative path w.r.t the location of this configuration file.

Step 2: run the evaluate pipeline

CUDA_VISIBLE_DEVICES="0,1,2,3" mpirun -np 4 --allow-run-as-root python PyLearn.py evaluate ExampleConf/conf_eval_hmnet_AMI

The decoding results could be found at ExampleConf/conf_eval_hmnet_AMI_conf~/run_1

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