# Neural Sentence Ordering Based on Constraint Graphs

arXiv: https://arxiv.org/abs/2101.11178 · HTML: https://mlsanity.com/paper/2101.11178/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://mlsanity.com/paper/1804.09541/) (2018)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules](https://mlsanity.com/paper/1806.09206/) (2018)
- [Neural Net Models for Open-Domain Discourse Coherence](https://mlsanity.com/paper/1606.01545/) (2016)
- [Deep Attentive Ranking Networks for Learning to Order Sentences](https://mlsanity.com/paper/2001.00056/) (2019)
- [Topological Sort for Sentence Ordering](https://mlsanity.com/paper/2005.00432/) (2020)
- [Graph-based Neural Sentence Ordering](https://mlsanity.com/paper/1912.07225/) (2019)
- [Neural Sentence Ordering](https://mlsanity.com/paper/1607.06952/) (2016)
- [End-to-End Neural Sentence Ordering Using Pointer Network](https://mlsanity.com/paper/1611.04953/) (2016)
- [ScriptWriter: Narrative-Guided Script Generation](https://mlsanity.com/paper/2005.10331/) (2020)

## Built on by (most cited first, 8 of 8)

- [Is Everything in Order? A Simple Way to Order Sentences](https://mlsanity.com/paper/2104.07064/) (2021)
- [STaCK: Sentence Ordering with Temporal Commonsense Knowledge](https://mlsanity.com/paper/2109.02247/) (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)
- [BERT4SO: Neural Sentence Ordering by Fine-tuning BERT](https://mlsanity.com/paper/2103.13584/) (2021)
- [Local and Global Context-Based Pairwise Models for Sentence Ordering](https://mlsanity.com/paper/2110.04291/) (2021)
- [Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding](https://mlsanity.com/paper/2208.09129/) (2022)
- [Learning Page Order in Shuffled WOO Releases](https://mlsanity.com/paper/2602.11040/) (2026)

## Abstract (arXiv)

Sentence ordering aims at arranging a list of sentences in the correct order. Based on the observation that sentence order at different distances may rely on different types of information, we devise a new approach based on multi-granular orders between sentences. These orders form multiple constraint graphs, which are then encoded by Graph Isomorphism Networks and fused into sentence representations. Finally, sentence order is determined using the order-enhanced sentence representations. Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance. The results demonstrate the advantage of considering multiple types of order information and using graph neural networks to integrate sentence content and order information for the task. Our code is available at https://github.com/DaoD/ConstraintGraph4NSO.