# Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks

arXiv: https://arxiv.org/abs/2010.06253 · HTML: https://mlsanity.com/paper/2010.06253/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 22 of 22)

- [An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics](https://mlsanity.com/paper/2207.00939/) (2022)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [A Survey on Long Text Modeling with Transformers](https://mlsanity.com/paper/2302.14502/) (2023)
- [HEGEL: Hypergraph Transformer for Long Document Summarization](https://mlsanity.com/paper/2210.04126/) (2022)
- [Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks](https://mlsanity.com/paper/2205.10282/) (2022)
- [Topic-Guided Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2110.11207/) (2021)
- [Leveraging Information Bottleneck for Scientific Document Summarization](https://mlsanity.com/paper/2110.01280/) (2021)
- [GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization](https://mlsanity.com/paper/2208.09982/) (2022)
- [GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state](https://mlsanity.com/paper/2211.10247/) (2022)
- [StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs](https://mlsanity.com/paper/2505.22950/) (2025)
- [TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation](https://mlsanity.com/paper/2206.00847/) (2022)
- [EDU-level Extractive Summarization with Varying Summary Lengths](https://mlsanity.com/paper/2210.04029/) (2022)
- [Abstractive Summarization Guided by Latent Hierarchical Document Structure](https://mlsanity.com/paper/2211.09458/) (2022)
- [Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level](https://mlsanity.com/paper/2405.10202/) (2024)
- [GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization](https://mlsanity.com/paper/2410.21315/) (2024)
- [Prompt-tuning for Clickbait Detection via Text Summarization](https://mlsanity.com/paper/2404.11206/) (2024)
- [Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures](https://mlsanity.com/paper/2204.11190/) (2022)
- [Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion](https://mlsanity.com/paper/2205.13108/) (2022)
- [Rethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approaches](https://mlsanity.com/paper/2508.00864/) (2025)
- [Graph Neural Networks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2106.06090/) (2021)
- [GAE-ISumm: Unsupervised Graph-Based Summarization of Indian Languages](https://mlsanity.com/paper/2212.12937/) (2022)
- [Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization](https://mlsanity.com/paper/2509.09852/) (2025)

## Abstract (arXiv)

Text summarization aims to compress a textual document to a short summary while keeping salient information. Extractive approaches are widely used in text summarization because of their fluency and efficiency. However, most of existing extractive models hardly capture inter-sentence relationships, particularly in long documents. They also often ignore the effect of topical information on capturing important contents. To address these issues, this paper proposes a graph neural network (GNN)-based extractive summarization model, enabling to capture inter-sentence relationships efficiently via graph-structured document representation. Moreover, our model integrates a joint neural topic model (NTM) to discover latent topics, which can provide document-level features for sentence selection. The experimental results demonstrate that our model not only substantially achieves state-of-the-art results on CNN/DM and NYT datasets but also considerably outperforms existing approaches on scientific paper datasets consisting of much longer documents, indicating its better robustness in document genres and lengths. Further discussions show that topical information can help the model preselect salient contents from an entire document, which interprets its effectiveness in long document summarization.