# Deep Attentive Ranking Networks for Learning to Order Sentences

arXiv: https://arxiv.org/abs/2001.00056 · HTML: https://mlsanity.com/paper/2001.00056/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2019.

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

- [Structural Pre-training for Dialogue Comprehension](https://mlsanity.com/paper/2105.10956/) (2021)
- [Is Everything in Order? A Simple Way to Order Sentences](https://mlsanity.com/paper/2104.07064/) (2021)
- [Towards Global Optimal Visual In-Context Learning Prompt Selection](https://mlsanity.com/paper/2405.15279/) (2024)
- [Task Compass: Scaling Multi-task Pre-training with Task Prefix](https://mlsanity.com/paper/2210.06277/) (2022)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Neural Sentence Ordering Based on Constraint Graphs](https://mlsanity.com/paper/2101.11178/) (2021)
- [STaCK: Sentence Ordering with Temporal Commonsense Knowledge](https://mlsanity.com/paper/2109.02247/) (2021)
- [Set-to-Sequence Methods in Machine Learning: a Review](https://mlsanity.com/paper/2103.09656/) (2021)
- [Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models](https://mlsanity.com/paper/2303.11120/) (2023)
- [Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering](https://mlsanity.com/paper/2108.10986/) (2021)
- [Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings](https://mlsanity.com/paper/2110.06446/) (2021)
- [A Novel Computational and Modeling Foundation for Automatic Coherence Assessment](https://mlsanity.com/paper/2310.00598/) (2023)
- [Kanbun-LM: Reading and Translating Classical Chinese in Japanese Methods by Language Models](https://mlsanity.com/paper/2305.12759/) (2023)
- [BERT4SO: Neural Sentence Ordering by Fine-tuning BERT](https://mlsanity.com/paper/2103.13584/) (2021)
- [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)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2110.04984/) (2021)
- [Pruned Graph Neural Network for Short Story Ordering](https://mlsanity.com/paper/2203.06778/) (2022)
- [Non-Autoregressive Sentence Ordering](https://mlsanity.com/paper/2310.12640/) (2023)
- [Set2Seq Transformer: Temporal and Position-Aware Set Representations for Sequential Multiple-Instance Learning](https://mlsanity.com/paper/2408.03404/) (2024)
- [STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning](https://mlsanity.com/paper/2506.19883/) (2025)
- [Learning Page Order in Shuffled WOO Releases](https://mlsanity.com/paper/2602.11040/) (2026)

## Abstract (arXiv)

We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention based transformer network to obtain an input order invariant representation of paragraphs. Moreover, it allows seamless training using a variety of ranking based loss functions, such as pointwise, pairwise, and listwise ranking. We apply our framework on two tasks: Sentence Ordering and Order Discrimination. Our framework outperforms various state-of-the-art methods on these tasks on a variety of evaluation metrics. We also show that it achieves better results when using pairwise and listwise ranking losses, rather than the pointwise ranking loss, which suggests that incorporating relative positions of two or more sentences in the loss function contributes to better learning.