# On the Contribution of Discourse Structure on Text Complexity Assessment

arXiv: https://arxiv.org/abs/1708.05800 · HTML: https://mlsanity.com/paper/1708.05800/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Streaming, Fast and Slow: Cognitive Load-Aware Streaming for Efficient LLM Serving](https://mlsanity.com/paper/2504.17999/) (2025)
- [A Novel Dependency Framework for Enhancing Discourse Data Analysis](https://mlsanity.com/paper/2407.12473/) (2024)

## Abstract (arXiv)

This paper investigates the influence of discourse features on text complexity assessment. To do so, we created two data sets based on the Penn Discourse Treebank and the Simple English Wikipedia corpora and compared the influence of coherence, cohesion, surface, lexical and syntactic features to assess text complexity. Results show that with both data sets coherence features are more correlated to text complexity than the other types of features. In addition, feature selection revealed that with both data sets the top most discriminating feature is a coherence feature.