ACL2020-GraphSum

GraphSum

Code for ACL2020 paper: Leveraging Graph to Improve Abstractive Multi-Document Summarization

Introduction

GraphSum is a multi-document abstractive summarization model which integrates explicit graph representations of documents such as lexical similarity and discourse graph into transformer-based end-to-end neural generation model. The model can leverage graph to better encode long input from multiple documents and model the intra- and cross-document relations more effectively. The model is proposed in ACL2020 paper Leveraging Graph to Improve Abstractive Multi-Document Summarization.

Dependencies

• Python3.6.9
• paddlepaddle-gpu==1.6.3.post107
• pyrouge==0.1.3
• sentencepiece==0.1.91

Data Preparation

We have already processed WikiSum and MultiNews dataset. The processed WikiSum dataset can be downloaded from https://graphsum.bj.bcebos.com/data/WikiSum_data_tfidf_40_paddle.tar.gz

The processed MultiNews dataset can be downloaded from https://graphsum.bj.bcebos.com/data/MultiNews_data_tfidf_30_paddle.tar.gz (unzipped, and set $TASK_DATA_PATH in ./model_config/graphsum_model_conf_local).

The sentencepiece vocab file can be downloaded from https://graphsum.bj.bcebos.com/vocab/spm9998_3.model (set $VOCAB_PATH in ./model_config/graphsum_model_conf_local).

To process the dataset and build graph by yourself, you can get the raw version of the WikiSum dataset from here and the ranked version of the dataset from here. The raw version of the MultiNews dataset can be obtained from this link

Our data preprocess code (contains the code to build similarity graph and topic graph) has also been open link.

Model Configuration

Global Configuration for GraphSum model, such as hidden size, num of layers, head size etc.:

./model_config/graphsum_config.json

Training and Testing Configurations, such as batch_size, num of training epoches, beam_size etc.

./model_config/graphsum_model_conf_local

Train

We use 8 Tesla-V100-32G GPUs to train our model. Directly runing the scripts as following to train the model:

Training on the WikiSum dataset:

./scripts/run_graphsum_local_wikisum.sh

Training on the MultiNews dataset:

./scripts/run_graphsum_local_multinews.sh

Test

After completing the training process, several checkpoints are stored, e.g. ./models/graphsum_wikisum/step_300000. You can run the following command to get the results on test set (only one GPU is required for testing):

Testing on the WikiSum dataset:

./scripts/predict_graphsum_local_wikisum.sh

Testing on the MultiNews dataset:

./scripts/predict_graphsum_local_multinews.sh

Results

dataset	ROUGE-1	ROUGE-2	ROUGE-L
MultiNews	45.71	17.12	41.99
WikiSum	43.23	27.25	40.99

We re-trained the model by tuning some parameters, and got the above results which were a little better than the results reported in the paper.
**Note that, the above ROUGEL-L results are sentence-level ROUGE-L scores which are the most widely used in summarization research.

Generated Summaries

The generated summaries for GraphSum on the WikiSum dataset can be downloaded https://graphsum.bj.bcebos.com/generated_summaries/graphsum_wikisum_results.tar.gz
The generated summaries for GraphSum on the MultiNews dataset can be downloaded https://graphsum.bj.bcebos.com/generated_summaries/graphsum_multinews_results.tar.gz

Trained Models

The trained GraphSum model on the WikiSum dataset can be downloaded from graphsum_wikisum_trained_model
The trained GraphSum model on the MultiNews dataset can be downloaded from graphsum_multinews_trained_model
To predict with our trained models, firstly unzip them and then set parameter $init_checkpoint in ./scripts/predict_graphsum_local_wikisum.sh and ./scripts/predict_graphsum_local_multinews.sh to the path of trained models, respectively.

Citation

If you find our code useful in your work, please cite the following paper:

@article{li2020leveraging,
title={Leveraging Graph to Improve Abstractive Multi-Document Summarization},
author={Li, Wei and Xiao, Xinyan and Liu, Jiachen and Wu, Hua and Wang, Haifeng and Du, Junping},
journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020},
publisher={Association for Computational Linguistics} }