# Graph-based Neural Sentence Ordering

arXiv: https://arxiv.org/abs/1912.07225 · HTML: https://mlsanity.com/paper/1912.07225/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2019.

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

- [Is Everything in Order? A Simple Way to Order Sentences](https://mlsanity.com/paper/2104.07064/) (2021)
- [Neural Sentence Ordering Based on Constraint Graphs](https://mlsanity.com/paper/2101.11178/) (2021)
- [SgSum: Transforming Multi-document Summarization into Sub-graph Selection](https://mlsanity.com/paper/2110.12645/) (2021)
- [STaCK: Sentence Ordering with Temporal Commonsense Knowledge](https://mlsanity.com/paper/2109.02247/) (2021)
- [TWAG: A Topic-Guided Wikipedia Abstract Generator](https://mlsanity.com/paper/2106.15135/) (2021)
- [Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models](https://mlsanity.com/paper/2303.11120/) (2023)
- [Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings](https://mlsanity.com/paper/2110.06446/) (2021)
- [InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs](https://mlsanity.com/paper/2307.00228/) (2023)
- [Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing](https://mlsanity.com/paper/2406.16529/) (2024)
- [BERT4SO: Neural Sentence Ordering by Fine-tuning BERT](https://mlsanity.com/paper/2103.13584/) (2021)
- [Getting the Most out of Simile Recognition](https://mlsanity.com/paper/2211.05984/) (2022)
- [A New Sentence Ordering Method Using BERT Pretrained Model](https://mlsanity.com/paper/2108.11994/) (2021)
- [Local and Global Context-Based Pairwise Models for Sentence Ordering](https://mlsanity.com/paper/2110.04291/) (2021)
- [Procedural Text Understanding via Scene-Wise Evolution](https://mlsanity.com/paper/2203.07600/) (2022)
- [GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization](https://mlsanity.com/paper/2408.10115/) (2024)
- [InsertGNN: Can Graph Neural Networks Outperform Humans in TOEFL Sentence Insertion Problem?](https://mlsanity.com/paper/2103.15066/) (2021)
- [Pruned Graph Neural Network for Short Story Ordering](https://mlsanity.com/paper/2203.06778/) (2022)
- [OrderSum: Semantic Sentence Ordering for Extractive Summarization](https://mlsanity.com/paper/2502.16180/) (2025)
- [SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents](https://mlsanity.com/paper/2606.18946/) (2026)

## Abstract (arXiv)

Sentence ordering is to restore the original paragraph from a set of sentences. It involves capturing global dependencies among sentences regardless of their input order. In this paper, we propose a novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network citeZhang:acl18 to accurately learn semantic representations of the sentences. Instead of assuming connections between all pairs of input sentences, we use entities that are shared among multiple sentences to make more expressive graph representations with less noise. Experimental results show that our proposed model outperforms the existing state-of-the-art systems on several benchmark datasets, demonstrating the effectiveness of our model. We also conduct a thorough analysis on how entities help the performance.