---
title: "大模型文本简化的篇章结构变化分析与评测 — 朱君辉 2026"
description: "This study examines how a large language model, GPT-4o, changes the discourse organization of Chinese news articles when it simplifies them."
authors: ["朱君辉", "杨尔弘", "朱浩楠", "杨麟儿"]
year: 2026
venue: "中文信息学报 (Journal of Chinese Information Processing)"
source: "http://jcip.cipsc.org.cn/article/id/2bc4b900-9adf-4f19-a8c5-5fcb6b62d85e"
licence: "Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/zhu-2026-discourse-structure-variation/"
---
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# 大模型文本简化的篇章结构变化分析与评测

**Authors:** 朱君辉, 杨尔弘, 朱浩楠, 杨麟儿 · **Year:** 2026 · **Venue:** 中文信息学报 (Journal of Chinese Information Processing) · **Licence:** Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报

**Links:** [Publisher's page](http://jcip.cipsc.org.cn/article/id/2bc4b900-9adf-4f19-a8c5-5fcb6b62d85e) · [PDF](http://jcip.cipsc.org.cn/cn/article/pdf/preview/2bc4b900-9adf-4f19-a8c5-5fcb6b62d85e.pdf)

## Summary
This study examines how a large language model, GPT-4o, changes the discourse organization of Chinese news articles when it simplifies them. Most existing simplification work evaluates only words and syntax, and assessments of LLMs rely mostly on subjective human ratings. The authors use Rhetorical Structure Theory (RST) to compare three versions of each article: the original, an expert simplification, and a GPT-4o rewrite. They describe the result as the first Chinese parallel discourse-simplification treebank. Each sentence is treated as one EDU, and annotators assigned nucleus–satellite labels and semantic relations. The authors also define nine quantitative indicators covering tree shape and relation properties. Statistical comparison shows that the model's discourse trees are flatter than the experts'. GPT-4o also tends to delete and merge content too aggressively, which causes it to lose core information and weakens referential links. Experts, by contrast, add transitions and restructure the text. The authors propose using these structural indicators for evaluation, structure-aware prompting and model self-correction.

## Key points
- **Data:** The base corpus is CNS (Chinese News Simplification Data), with 50 originals and 50 expert simplifications drawn from textbooks for beginner learners. The authors added 50 GPT-4o outputs (temperature 0.7, top-p 1.0), giving 150 texts in total.
- **Annotation:**
  - The relation set has 34 types, extended from classic RST with several new categories.
  - Annotation was done in RSTTool 3.42 and exported as .rs3 files.
  - Two annotators reached Cohen's Kappa ≈ 0.82.
  - The treebank contains about 6,250 sentence nodes and 3,162 relations, with an average tree depth of about 4.8.
- **Relation counts:**

  | Version | Relation types | Relation instances |
  |---|---|---|
  | Originals | 34 | 1,465 |
  | Expert simplifications | 33 | 931 |
  | GPT-4o | 32 | 794 |

- **Nine indicators:**
  - Tree-level: maximum depth, mean depth, depth variance, shallow-sentence ratio, sentences per level.
  - Relation-level: nucleus count, satellite count, relation diversity, logical coherence.
- **Statistics:** The authors used Kruskal-Wallis H tests with Dunn's post hoc comparisons, and ANOVA for relation diversity. Eight of the nine indicators differed significantly across the three versions; logical coherence was the exception. The authors attribute GPT-4o's weaknesses to four causes: it misidentifies core units, has weak sensitivity to cohesion, over-relies on coordination, and does not compensate for content it removes.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/topics/discourse-analysis/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{2026,
  title = {{大模型文本简化的篇章结构变化分析与评测}},
  author = {朱君辉 and 杨尔弘 and 朱浩楠 and 杨麟儿},
  journal = {中文信息学报 (Journal of Chinese Information Processing)},
  year = {2026},
  url = {http://jcip.cipsc.org.cn/article/id/2bc4b900-9adf-4f19-a8c5-5fcb6b62d85e}
}
```

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