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
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation Introduction This is a PyTorch

XMed-Lab 30 Sep 23, 2022
Code to produce syntactic representations that can be used to study syntax processing in the human brain

Can fMRI reveal the representation of syntactic structure in the brain? The code base for our paper on understanding syntactic representations in the

Aniketh Janardhan Reddy 4 Dec 18, 2022
[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.

TransMaS This repository is the official pytorch implementation of the following paper: NIPS2021 Mixed Supervised Object Detection by TransferringMask

BCMI 49 Jul 27, 2022
DeRF: Decomposed Radiance Fields

DeRF: Decomposed Radiance Fields Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi Links Paper Project Page Abstract

UBC Computer Vision Group 24 Dec 02, 2022
Code for "Primitive Representation Learning for Scene Text Recognition" (CVPR 2021)

Primitive Representation Learning Network (PREN) This repository contains the code for our paper accepted by CVPR 2021 Primitive Representation Learni

Ruijie Yan 76 Jan 02, 2023
Cweqgen - The CW Equation Generator

The CW Equation Generator The cweqgen (pronouced like "Queck-Jen") package provi

2 Jan 15, 2022
A Factor Model for Persistence in Investment Manager Performance

Factor-Model-Manager-Performance A Factor Model for Persistence in Investment Manager Performance I apply methods and processes similar to those used

Omid Arhami 1 Dec 01, 2021
ObsPy: A Python Toolbox for seismology/seismological observatories.

ObsPy is an open-source project dedicated to provide a Python framework for processing seismological data. It provides parsers for common file formats

ObsPy 979 Jan 07, 2023
Deformable DETR is an efficient and fast-converging end-to-end object detector.

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

2k Jan 05, 2023
免费获取http代理并生成proxifier配置文件

freeproxy 免费获取http代理并生成proxifier配置文件 公众号：台下言书 工具说明：https://mp.weixin.qq.com/s?__biz=MzIyNDkwNjQ5Ng==&mid=2247484425&idx=1&sn=56ccbe130822aa35038095317

说书人 32 Mar 25, 2022
Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
.NET bindings for the Pytorch engine

TorchSharp TorchSharp is a .NET library that provides access to the library that powers PyTorch. It is a work in progress, but already provides a .NET

Matteo Interlandi 17 Aug 30, 2021
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration Stefan Abi-Karam*, Yuqi He*, Rishov Sarkar*, Lakshmi Sathidevi, Zihang Qiao, Co

Sharc-Lab 19 Dec 15, 2022
Implementation of ICCV19 Paper "Learning Two-View Correspondences and Geometry Using Order-Aware Network"

OANet implementation Pytorch implementation of OANet for ICCV'19 paper "Learning Two-View Correspondences and Geometry Using Order-Aware Network", by

Jiahui Zhang 225 Dec 05, 2022
The fundamental package for scientific computing with Python.

NumPy is the fundamental package needed for scientific computing with Python. Website: https://www.numpy.org Documentation: https://numpy.org/doc Mail

NumPy 22.4k Jan 09, 2023
Proof of concept GnuCash Webinterface

Proof of Concept GnuCash Webinterface This may one day be a something truly great. Milestones [ ] Browse accounts and view transactions [ ] Record sim

Josh 14 Dec 28, 2022
A minimal implementation of face-detection models using flask, gunicorn, nginx, docker, and docker-compose

Face-Detection-flask-gunicorn-nginx-docker This is a simple implementation of dockerized face-detection restful-API implemented with flask, Nginx, and

Pooya-Mohammadi 30 Dec 17, 2022
Sound Event Detection with FilterAugment

Sound Event Detection with FilterAugment Official implementation of Heavily Augmented Sound Event Detection utilizing Weak Predictions (DCASE2021 Chal

43 Aug 28, 2022
An implementation of the Contrast Predictive Coding (CPC) method to train audio features in an unsupervised fashion.

CPC_audio This code implements the Contrast Predictive Coding algorithm on audio data, as described in the paper Unsupervised Pretraining Transfers we

Meta Research 283 Dec 30, 2022
Mmdetection3d Noted - MMDetection3D is an open source object detection toolbox based on PyTorch

MMDetection3D is an open source object detection toolbox based on PyTorch

Jiangjingwen 13 Jan 06, 2023