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.
- We use pytorch_geometric for GNN implementation.

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

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

Related tags

Overview

DDAMS

Requirements

Data

Reproduce Results

download checkpoints

translate

remove tags

test rouge score

ROUGE score

From Scratch

For AMI

Preprocess

Train

Translate

For ICSI

Preprocess

Train

Translate

Test Rouge

Owner

xcfeng

The official implementation of Autoregressive Image Generation using Residual Quantization (CVPR '22)

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

An algorithm study of the 6th iOS 10 set of Boost Camp Web Mobile

Code repository for the work "Multi-Domain Incremental Learning for Semantic Segmentation", accepted at WACV 2022

A curated list of awesome Active Learning

공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다.

An open source object detection toolbox based on PyTorch

Pytorch implementation of “Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement”

hySLAM is a hybrid SLAM/SfM system designed for mapping

Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches

[CVPRW 2022] Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network

Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation Learning Source Code

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Visual Tracking by TridenAlign and Context Embedding

Supervised multi-SNE (S-multi-SNE): Multi-view visualisation and classification

DISTIL: Deep dIverSified inTeractIve Learning.

A Novel Plug-in Module for Fine-grained Visual Classification

Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models

The end-to-end platform for building voice products at scale

Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.