Pengyi Hao^*1, Cunqi Wu^*1, Cong Bai^**1,
^*Equal contribution. ^**Corresponding author.
¹Zhejiang University of Technology

Extractive summarization aims to select important sentences from the document to generate a summary. However, current extractive document summarization methods fail to fully consider the semantic information among sentences and the various relations in the entire document. Therefore, a novel end-to-end framework named hierarchical heterogeneous graph learning for document summarization (HHGraphSum) is proposed in this paper. In this framework, a hierarchical heterogeneous graph is constructed for the whole document, where the representation of sentences is learnt by several levels of graph neural network. The combination of single- direction message passing and bidirectional message passing helps graph learning obtain effective relations among sentences and words. For capturing the rich semantic information, space-time collaborative learning is designed to generate the primary features of sentences which are enhanced in graph learning. For generating a less redundant and more precise summary, a LSTM based predictor and a blocking strategy are explored.Evaluations both on a single-document dataset and a multi-document dataset demonstrate the effectiveness of the HHGraphSum.The code of HHGraphSum is available on Github: https://github.com/Devin100086/HHGraphSum

• [11/8] 🎉 Congratulation! our paper has been accepted for publication in DISPLAYS.
• [4/13] 🔥We have released HHGraphSum code.

Our environment has been tested on Linux, CUDA 11.8 with one 2080Ti.

1. Clone our repo and create conda environment

   git clone https://github.com/Devin100086/HHGraphSum.git && cd HHGraphSum
   
   # You  pip environment
   pip install torch==2.0.1 cu118 torchvision==0.15.2 cu118 --extra-index-url https://download.pytorch.org/whl/cu118
   pip install  dgl -f https://data.dgl.ai/wheels/torch-2.1/cu118/repo.html
   

2. Download the datasets and pretrained model

   • CNNDM
   • MultiNews
   • Pretrained model Glove

3. prepare the data and train the model

      sh bash/PrepareDataset.sh
      sh bash/train.sh

4. prepare the data and train the model

   sh bash/test.sh

This work is partially supported by National Natural Science Foundation of China under Grant No. U20A20196, and Zhejiang Province Natural Science Foundation under Grant No. LR21F020002.

@article{hao2024hhgraphsum,
 title={HHGraphSum: Hierarchical heterogeneous graph learning for extractive document summarization},
 author={Hao, Pengyi and Wu, Cunqi and Bai, Cong},
 journal={Displays},
 pages={102884},
 year={2024},
 publisher={Elsevier}
}