# Multiplex Graph Neural Network for Extractive Text Summarization

arXiv: https://arxiv.org/abs/2108.12870 · HTML: https://mlsanity.com/paper/2108.12870/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [Discourse-Aware Neural Extractive Text Summarization](https://mlsanity.com/paper/1910.14142/) (2019)
- [Unsupervised Attributed Multiplex Network Embedding](https://mlsanity.com/paper/1911.06750/) (2019)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://mlsanity.com/paper/2004.12393/) (2020)
- [HDMI: High-order Deep Multiplex Infomax](https://mlsanity.com/paper/2102.07810/) (2021)
- [Learning to Extract Coherent Summary via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.07036/) (2018)
- [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports](https://mlsanity.com/paper/2004.12274/) (2020)
- [Neural Document Summarization by Jointly Learning to Score and Select Sentences](https://mlsanity.com/paper/1807.02305/) (2018)
- [Graph-based Neural Multi-Document Summarization](https://mlsanity.com/paper/1706.06681/) (2017)
- [BanditSum: Extractive Summarization as a Contextual Bandit](https://mlsanity.com/paper/1809.09672/) (2018)
- [Searching for Effective Neural Extractive Summarization: What Works and What's Next](https://mlsanity.com/paper/1907.03491/) (2019)
- [Neural Extractive Text Summarization with Syntactic Compression](https://mlsanity.com/paper/1902.00863/) (2019)
- [Network of Tensor Time Series](https://mlsanity.com/paper/2102.07736/) (2021)
- [Neural Latent Extractive Document Summarization](https://mlsanity.com/paper/1808.07187/) (2018)
- [Jointly Extracting and Compressing Documents with Summary State Representations](https://mlsanity.com/paper/1904.02020/) (2019)
- [Cross-Domain Labeled LDA for Cross-Domain Text Classification](https://mlsanity.com/paper/1809.05820/) (2018)

## Built on by (most cited first, 19 of 19)

- [A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models](https://mlsanity.com/paper/2406.11289/) (2024)
- [Graph Communal Contrastive Learning](https://mlsanity.com/paper/2110.14863/) (2021)
- [HEGEL: Hypergraph Transformer for Long Document Summarization](https://mlsanity.com/paper/2210.04126/) (2022)
- [Heterogeneous Contrastive Learning for Foundation Models and Beyond](https://mlsanity.com/paper/2404.00225/) (2024)
- [X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning](https://mlsanity.com/paper/2109.03560/) (2021)
- [Retrieval Based Time Series Forecasting](https://mlsanity.com/paper/2209.13525/) (2022)
- [COIN: Co-Cluster Infomax for Bipartite Graphs](https://mlsanity.com/paper/2206.00006/) (2022)
- [PyG-SSL: A Graph Self-Supervised Learning Toolkit](https://mlsanity.com/paper/2412.21151/) (2024)
- [STERLING: Synergistic Representation Learning on Bipartite Graphs](https://mlsanity.com/paper/2302.05428/) (2023)
- [FairGen: Towards Fair Graph Generation](https://mlsanity.com/paper/2303.17743/) (2023)
- [Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation](https://mlsanity.com/paper/2403.11960/) (2024)
- [ARIEL: Adversarial Graph Contrastive Learning](https://mlsanity.com/paper/2208.06956/) (2022)
- [EDU-level Extractive Summarization with Varying Summary Lengths](https://mlsanity.com/paper/2210.04029/) (2022)
- [Topic-Selective Graph Network for Topic-Focused Summarization](https://mlsanity.com/paper/2302.13106/) (2023)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [Enhancing Coherence of Extractive Summarization with Multitask Learning](https://mlsanity.com/paper/2305.12851/) (2023)
- [Unsupervised Extractive Summarization with Learnable Length Control Strategies](https://mlsanity.com/paper/2312.06901/) (2023)
- [Mapping Text to Multiplex Graph: Prompt Compression as L'evy Walk-Guided Graph Pruning](https://mlsanity.com/paper/2607.01241/) (2026)
- [Semantic-Enhanced Cross-Modal Place Recognition for Robust Robot Localization](https://mlsanity.com/paper/2509.13474/) (2025)

## Abstract (arXiv)

Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, sentence embedding plays an important role. Recent studies have leveraged graph neural networks to capture the inter-sentential relationship (e.g., the discourse graph) to learn contextual sentence embedding. However, those approaches neither consider multiple types of inter-sentential relationships (e.g., semantic similarity & natural connection), nor model intra-sentential relationships (e.g, semantic & syntactic relationship among words). To address these problems, we propose a novel Multiplex Graph Convolutional Network (Multi-GCN) to jointly model different types of relationships among sentences and words. Based on Multi-GCN, we propose a Multiplex Graph Summarization (Multi-GraS) model for extractive text summarization. Finally, we evaluate the proposed models on the CNN/DailyMail benchmark dataset to demonstrate the effectiveness of our method.