# Skeleton-based Coherence Modeling in Narratives

arXiv: https://arxiv.org/abs/2604.02451 · HTML: https://mlsanity.com/paper/2604.02451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 2 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Visual Storytelling](https://mlsanity.com/paper/1604.03968/) (2016)
- [A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://mlsanity.com/paper/1808.06945/) (2018)
- [Story Generation from Sequence of Independent Short Descriptions](https://mlsanity.com/paper/1707.05501/) (2017)
- [Neural Net Models for Open-Domain Discourse Coherence](https://mlsanity.com/paper/1606.01545/) (2016)
- [Coherence Modeling of Asynchronous Conversations: A Neural Entity Grid Approach](https://mlsanity.com/paper/1805.02275/) (2018)
- [Coherence Models for Dialogue](https://mlsanity.com/paper/1806.08044/) (2018)

## Abstract (arXiv)

Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work in using neural networks to extract a skeleton from one sentence, and then use that skeleton to generate the next sentence for coherent narrative story generation. In this project, we aim to study if the consistency of skeletons across subsequent sentences is a good metric to characterize the coherence of a given body of text. We propose a new Sentence/Skeleton Similarity Network (SSN) for modeling coherence across pairs of sentences, and show that this network performs much better than baseline similarity techniques like cosine similarity and Euclidean distance. Although skeletons appear to be promising candidates for modeling coherence, our results show that sentence-level models outperform those on skeletons for evaluating textual coherence, thus indicating that the current state-of-the-art coherence modeling techniques are going in the right direction by dealing with sentences rather than their sub-parts.