# Pruned Graph Neural Network for Short Story Ordering

arXiv: https://arxiv.org/abs/2203.06778 · HTML: https://mlsanity.com/paper/2203.06778/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 13 Mar 2022.

## Builds on (most cited first, 25 of 25)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension](https://mlsanity.com/paper/1804.09541/) (2018)
- [Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://mlsanity.com/paper/1703.04826/) (2017)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [Graph-to-Sequence Learning using Gated Graph Neural Networks](https://mlsanity.com/paper/1806.09835/) (2018)
- [SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models](https://mlsanity.com/paper/2002.06652/) (2020)
- [Graph-based Neural Multi-Document Summarization](https://mlsanity.com/paper/1706.06681/) (2017)
- [What Makes A Good Story? Designing Composite Rewards for Visual Storytelling](https://mlsanity.com/paper/1909.05316/) (2019)
- [N-ary Relation Extraction using Graph State LSTM](https://mlsanity.com/paper/1808.09101/) (2018)
- [Neural Net Models for Open-Domain Discourse Coherence](https://mlsanity.com/paper/1606.01545/) (2016)
- [Stochastic Answer Networks for Machine Reading Comprehension](https://mlsanity.com/paper/1712.03556/) (2017)
- [Sentence-State LSTM for Text Representation](https://mlsanity.com/paper/1805.02474/) (2018)
- [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)
- [Sort Story: Sorting Jumbled Images and Captions into Stories](https://mlsanity.com/paper/1606.07493/) (2016)
- [Using BERT Encoding and Sentence-Level Language Model for Sentence Ordering](https://mlsanity.com/paper/2108.10986/) (2021)
- [A New Sentence Ordering Method Using BERT Pretrained Model](https://mlsanity.com/paper/2108.11994/) (2021)

## Abstract (arXiv)

Text coherence is a fundamental problem in natural language generation and understanding. Organizing sentences into an order that maximizes coherence is known as sentence ordering. This paper is proposing a new approach based on the graph neural network approach to encode a set of sentences and learn orderings of short stories. We propose a new method for constructing sentence-entity graphs of short stories to create the edges between sentences and reduce noise in our graph by replacing the pronouns with their referring entities. We improve the sentence ordering by introducing an aggregation method based on majority voting of state-of-the-art methods and our proposed one. Our approach employs a BERT-based model to learn semantic representations of the sentences. The results demonstrate that the proposed method significantly outperforms existing baselines on a corpus of short stories with a new state-of-the-art performance in terms of Perfect Match Ratio (PMR) and Kendall's Tau (Tau) metrics. More precisely, our method increases PMR and Tau criteria by more than 5% and 4.3%, respectively. These outcomes highlight the benefit of forming the edges between sentences based on their cosine similarity. We also observe that replacing pronouns with their referring entities effectively encodes sentences in sentence-entity graphs.