Codes for our IJCAI21 paper: Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Related tags

Deep LearningDDAMS
Overview

DDAMS

This is the pytorch code for our IJCAI 2021 paper Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization [Arxiv Preprint].

Requirements

  • We use Conda python 3.7 and strongly recommend that you create a new environment: conda create -n ddams python=3.7.
  • Run the following command: pip install -r requirements.txt.

Data

You can download data here, put the data under the project dir DDAMS/data/xxx.

  • data/ami
    • data/ami/ami: preprocessed meeting data
    • data/ami/ami_qg: pseudo summarization data.
    • data/ami/ami_reference: golden reference for test file.
  • data/icsi
    • data/icsi/icsi: preprocessed meeting data
    • data/icsi/icsi_qg: pseudo summarization data.
    • data/icsi/icsi_reference: golden reference for test file.
  • data/glove: pre-trained word embedding glove.6B.300d.txt.

Reproduce Results

You can follow the following steps to reproduce the best results in our paper.

download checkpoints

Download checkpoints here. Put the checkpoints, including AMI.pt and ICSI.pt, under the project dir DDAMS/models/xx.pt.

translate

Produce final summaries.

For AMI, we can get summaries/ami_summary.txt.

CUDA_VISIBLE_DEVICES=X python translate.py -batch_size 1 \
               -src data/ami/ami/test.src \
               -tgt data/ami/ami/test.tgt \
               -seg data/ami/ami/test.seg \
               -speaker data/ami/ami/test.speaker \
               -relation data/ami/ami/test.relation \
               -beam_size 10 \
               -share_vocab \
               -dynamic_dict \
               -replace_unk \
               -model models/AMI.pt \
               -output summaries/ami_summary.txt \
               -block_ngram_repeat 3 \
               -gpu 0 \
               -min_length 280 \
               -max_length 450

For ICSI, we can get summaries/icsi_summary.txt.

CUDA_VISIBLE_DEVICES=x python translate.py -batch_size 1 \
               -src data/icsi/icsi/test.src \
               -seg data/icsi/icsi/test.seg \
               -speaker data/icsi/icsi/test.speaker \
               -relation data/icsi/icsi/test.relation \
               -beam_size 10 \
               -share_vocab \
               -dynamic_dict \
               -replace_unk \
               -model models/ICSI.pt \
               -output summaries/icsi_summary.txt \
               -block_ngram_repeat 3 \
               -gpu 0 \
               -min_length 400 \
               -max_length 550

remove tags

<t> and </t> will raise errors for ROUGE test. So we should first remove them. (following OpenNMT)

sed -i 's/ <\/t>//g' summaries/ami_summary.txt
sed -i 's/<t> //g' summaries/ami_summary.txt
sed -i 's/ <\/t>//g' summaries/icsi_summary.txt
sed -i 's/<t> //g' summaries/icsi_summary.txt

test rouge score

  • Change pyrouge.Rouge155() to your local path.

Output format >> ROUGE(1/2/L): xx.xx-xx.xx-xx.xx

python test_rouge.py -c summaries/ami_summary.txt
python test_rouge_icsi.py -c summaries/icsi_summary.txt

ROUGE score

You will get following ROUGE scores.

ROUGE-1 ROUGE-2 ROUGE-L
AMI 53.15 22.32 25.67
ICSI 40.41 11.02 19.18

From Scratch

For AMI

Preprocess

(1) Preprocess AMI dataset.

python preprocess.py -train_src data/ami/ami/train.src \
                     -train_tgt data/ami/ami/train.tgt \
                     -train_seg data/ami/ami/train.seg \
                     -train_speaker data/ami/ami/train.speaker \
                     -train_relation data/ami/ami/train.relation \
                     -valid_src data/ami/ami/valid.src \
                     -valid_tgt data/ami/ami/valid.tgt \
                     -valid_seg data/ami/ami/valid.seg \
                     -valid_speaker data/ami/ami/valid.speaker \
                     -valid_relation data/ami/ami/valid.relation \
                     -save_data data/ami/AMI \
                     -dynamic_dict \
                     -share_vocab \
                     -lower \
                     -overwrite

(2) Create pre-trained word embeddings.

python embeddings_to_torch.py -emb_file_both data/glove/glove.6B.300d.txt \
-dict_file data/ami/AMI.vocab.pt \
-output_file data/ami/ami_embeddings

(3) Preprocess pseudo summarization dataset.

python preprocess.py -train_src data/ami/ami_qg/train.src \
                     -train_tgt data/ami/ami_qg/train.tgt \
                     -train_seg data/ami/ami_qg/train.seg \
                     -train_speaker data/ami/ami_qg/train.speaker \
                     -train_relation data/ami/ami_qg/train.relation \
                     -save_data data/ami/AMIQG \
                     -lower \
                     -overwrite \
                     -shard_size 500 \
                     -dynamic_dict \
                     -share_vocab

Train

(1) we first pre-train our DDAMS on the pseudo summarization dataset.

  • run the following command to save config file (-save_config).
  • remove -save_config and rerun the command to start the training process.
CUDA_VISIBLE_DEVICES=X python train.py -save_model ami_qg_pretrain/AMI_qg\
           -data data/ami/AMIQG \
           -speaker_type ami \
           -batch_size 64 \
           -learning_rate 0.001 \
           -share_embeddings \
           -share_decoder_embeddings \
           -copy_attn \
           -reuse_copy_attn \
           -report_every 30 \
           -encoder_type hier3 \
           -global_attention general \
           -save_checkpoint_steps 500 \
           -start_decay_steps 1500 \
           -pre_word_vecs_enc data/ami/ami_embeddings.enc.pt \
           -pre_word_vecs_dec data/ami/ami_embeddings.dec.pt \
           -log_file logs/ami_qg_pretrain.txt \
           -save_config logs/ami_qg_pretrain.txt

(2) fine-tuning on AMI.

CUDA_VISIBLE_DEVICES=X python train.py -save_model ami_final/AMI \
           -data data/ami/AMI \
           -speaker_type ami \
           -train_from ami_qg_pretrain/xxx.pt  \
           -reset_optim all \
           -batch_size 1 \
           -learning_rate 0.0005 \
           -share_embeddings \
           -share_decoder_embeddings \
           -copy_attn \
           -reuse_copy_attn \
           -encoder_type hier3 \
           -global_attention general \
           -dropout 0.5 \
           -attention_dropout 0.5 \
           -start_decay_steps 500 \
           -decay_steps 500 \
           -log_file logs/ami_final.txt \
           -save_config logs/ami_final.txt

Translate

CUDA_VISIBLE_DEVICES=X python translate.py -batch_size 1 \
               -src data/ami/ami/test.src \
               -tgt data/ami/ami/test.tgt \
               -seg data/ami/ami/test.seg \
               -speaker data/ami/ami/test.speaker \
               -relation data/ami/ami/test.relation \
               -beam_size 10 \
               -share_vocab \
               -dynamic_dict \
               -replace_unk \
               -model xxx.pt \
               -output xxx.txt \
               -block_ngram_repeat 3 \
               -gpu 0 \
               -min_length 280 \
               -max_length 450

For ICSI

Preprocess

(1) Preprocess ICSI dataset.

python preprocess.py -train_src data/icsi/icsi/train.src \
                     -train_tgt data/icsi/icsi/train.tgt \
                     -train_seg data/icsi/icsi/train.seg \
                     -train_speaker data/icsi/icsi/train.speaker \
                     -train_relation data/icsi/icsi/train.relation \
                     -valid_src data/icsi/icsi/valid.src \
                     -valid_tgt data/icsi/icsi/valid.tgt \
                     -valid_seg data/icsi/icsi/valid.seg \
                     -valid_speaker data/icsi/icsi/valid.speaker \
                     -valid_relation data/icsi/icsi/valid.relation \
                     -save_data data/icsi/ICSI \
                     -src_seq_length 20000 \
                     -src_seq_length_trunc 20000 \
                     -tgt_seq_length 700 \
                     -tgt_seq_length_trunc 700 \
                     -dynamic_dict \
                     -share_vocab \
                     -lower \
                     -overwrite

(2) Create pre-trained word embeddings.

python embeddings_to_torch.py -emb_file_both data/glove/glove.6B.300d.txt \
-dict_file data/icsi/ICSI.vocab.pt \
-output_file data/icsi/icsi_embeddings

(3) Preprocess pseudo summarization dataset.

python preprocess.py -train_src data/icsi/icsi_qg/train.src \
                     -train_tgt data/icsi/icsi_qg/train.tgt \
                     -train_seg data/icsi/icsi_qg/train.seg \
                     -train_speaker data/icsi/icsi_qg/train.speaker \
                     -train_relation data/icsi/icsi_qg/train.relation \
                     -save_data data/icsi/ICSIQG \
                     -lower \
                     -overwrite \
                     -shard_size 500 \
                     -dynamic_dict \
                     -share_vocab

Train

(1) pre-training.

CUDA_VISIBLE_DEVICES=X python train.py -save_model icsi_qg_pretrain/ICSI \
           -data data/icsi/ICSIQG \
           -speaker_type icsi \
           -batch_size 64 \
           -learning_rate 0.001 \
           -share_embeddings \
           -share_decoder_embeddings \
           -copy_attn \
           -reuse_copy_attn \
           -report_every 30 \
           -encoder_type hier3 \
           -global_attention general \
           -save_checkpoint_steps 500 \
           -start_decay_steps 1500 \
           -pre_word_vecs_enc data/icsi/icsi_embeddings.enc.pt \
           -pre_word_vecs_dec data/icsi/icsi_embeddings.dec.pt \
           -log_file logs/icsi_qg_pretrain.txt \
           -save_config logs/icsi_qg_pretrain.txt

(2) fine-tuning on ICSI.

CUDA_VISIBLE_DEVICES=X python train.py -save_model icsi_final/ICSI \
           -data data/icsi/ICSI \
           -speaker_type icsi \
           -train_from icsi_qg_pretrain/xxx.pt  \
           -reset_optim all \
           -batch_size 1 \
           -learning_rate 0.0005 \
           -share_embeddings \
           -share_decoder_embeddings \
           -copy_attn \
           -reuse_copy_attn \
           -encoder_type hier3 \
           -global_attention general \
           -dropout 0.5 \
           -attention_dropout 0.5 \
           -start_decay_steps 1000 \
           -decay_steps 100 \
           -save_checkpoint_steps 50 \
           -valid_steps 50 \
           -log_file logs/icsi_final.txt \
           -save_config logs/icsi_final.txt

Translate

CUDA_VISIBLE_DEVICES=x python translate.py -batch_size 1 \
               -src data/icsi/icsi/test.src \
               -seg data/icsi/icsi/test.seg \
               -speaker data/icsi/icsi/test.speaker \
               -relation data/icsi/icsi/test.relation \
               -beam_size 10 \
               -share_vocab \
               -dynamic_dict \
               -replace_unk \
               -model xxx.pt \
               -output xxx.txt \
               -block_ngram_repeat 3 \
               -gpu 0 \
               -min_length 400 \
               -max_length 550

Test Rouge

(1) Before ROUGE test, we should first remove special tags: .

sed -i 's/ <\/t>//g' xxx.txt
sed -i 's/<t> //g' xxx.txt

(2) Test rouge

python test_rouge.py -c summaries/xxx.txt
python test_rouge_icsi.py -c summaries/xxx.txt
Owner
xcfeng
Ph.D. candidate working on Summarization.
xcfeng
GitHub Repository
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

Bogireddy Sai Prasanna Teja Reddy 103 Dec 29, 2022
Python library for science observations from the James Webb Space Telescope

JWST Calibration Pipeline JWST requires Python 3.7 or above and a C compiler for dependencies. Linux and MacOS platforms are tested and supported. Win

Space Telescope Science Institute 386 Dec 30, 2022
Short and long time series classification using convolutional neural networks

time-series-classification Short and long time series classification via convolutional neural networks In this project, we present a novel framework f

35 Oct 22, 2022
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
This repository contains a Ruby API for utilizing TensorFlow.

tensorflow.rb Description This repository contains a Ruby API for utilizing TensorFlow. Linux CPU Linux GPU PIP Mac OS CPU Not Configured Not Configur

somatic labs 825 Dec 26, 2022
Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion

Feature-Style Encoder for Style-Based GAN Inversion Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion. Code will

InterDigital 63 Jan 03, 2023
NALSM: Neuron-Astrocyte Liquid State Machine

NALSM: Neuron-Astrocyte Liquid State Machine This package is a Tensorflow implementation of the Neuron-Astrocyte Liquid State Machine (NALSM) that int

Computational Brain Lab 4 Nov 28, 2022
PyTorch module to use OpenFace's nn4.small2.v1.t7 model

OpenFace for Pytorch Disclaimer: This codes require the input face-images that are aligned and cropped in the same way of the original OpenFace. * I m

Pete Tae-hoon Kim 176 Dec 12, 2022
Inferring Lexicographically-Ordered Rewards from Preferences

Inferring Lexicographically-Ordered Rewards from Preferences Code author: Alihan Hüyük ([e

Alihan Hüyük 1 Feb 13, 2022
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Music Source Separation with Channel-wise Subband Phase Aware ResUnet (CWS-PResUNet) Introduction This repo contains the pretrained Music Source Separ

Lau 100 Dec 25, 2022
This is just a funny project that we want to see AutoEncoder (AE) can actually work to enhance the features we want

Funny_muscle_enhancer :) 1.Discription: This is just a funny project that we want to see AutoEncoder (AE) can actually work on the some features. We w

Jing-Yao Chen (Jacob) 8 Oct 01, 2022
Pre-Trained Image Processing Transformer (IPT)

Pre-Trained Image Processing Transformer (IPT) By Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Cha

HUAWEI Noah's Ark Lab 332 Dec 18, 2022
BASH - Biomechanical Animated Skinned Human

We developed a method animating a statistical 3D human model for biomechanical analysis to increase accessibility for non-experts, like patients, athletes, or designers.

Machine Learning and Data Analytics Lab FAU 66 Nov 19, 2022
根据midi文件演奏“风物之诗琴”的脚本 "Windsong Lyre" auto play

Genshin-lyre-auto-play 简体中文 | English 简介 根据midi文件演奏“风物之诗琴”的脚本。由Python驱动,在此承诺， ⚠️ 项目内绝不含任何能够引起安全问题的代码。 前排提示：所有键盘在动但是原神没反应的都是因为没有管理员权限，双击run.bat或者以管理员模式

御坂17032号 386 Jan 01, 2023
Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Codebase for Amodal Segmentation through Out-of-Task andOut-of-Distribution Generalization with a Bayesian Model

Yihong Sun 12 Nov 15, 2022
This is an official implementation of the High-Resolution Transformer for Dense Prediction.

High-Resolution Transformer for Dense Prediction Introduction This is the official implementation of High-Resolution Transformer (HRT). We present a H

HRNet 403 Dec 13, 2022
Adversarial Self-Defense for Cycle-Consistent GANs

Adversarial Self-Defense for Cycle-Consistent GANs This is the official implementation of the CycleGAN robust to self-adversarial attacks used in pape

Dina Bashkirova 10 Oct 10, 2022
AntroPy: entropy and complexity of (EEG) time-series in Python

AntroPy is a Python 3 package providing several time-efficient algorithms for computing the complexity of time-series. It can be used for example to e

Raphael Vallat 153 Dec 27, 2022
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Feng 2 Nov 19, 2021