[Paper] Hierarchical Ranking Neural Network for Long Document Readability Assessment

Source: arXiv - 2511.21473v1

Overview

The paper introduces a Hierarchical Ranking Neural Network (HRNN) that evaluates how easy or hard a long document is to read. By first judging the difficulty of individual sentences and then aggregating those judgments, the model captures both fine‑grained semantics and the overall structure of the text—something most existing readability tools overlook.

Key Contributions

• Bidirectional sentence‑level readability estimator that highlights semantically rich regions in a document.
• Hierarchical aggregation: sentence predictions are fed into a document‑level classifier, preserving contextual cues across the whole text.
• Pairwise sorting loss that explicitly models the ordinal nature of readability levels (e.g., “easy” < “medium” < “hard”) via label subtraction.
• Cross‑lingual validation on both Chinese and English corpora, showing the approach works across languages with different writing systems.
• Competitive performance: consistently outperforms traditional readability formulas (e.g., Flesch‑Kincaid) and recent deep‑learning baselines.

Methodology

1. Sentence Encoder – Each sentence is passed through a bidirectional transformer (or BiLSTM) to capture left‑ and right‑context information. The encoder produces a dense vector representing the sentence’s semantic richness.
2. Sentence‑Level Classifier – A lightweight feed‑forward head predicts a readability label for every sentence (e.g., 1‑5).
3. Document Encoder – Sentence vectors, now annotated with their predicted difficulty, are fed into a second‑level encoder that models the sequence of sentences, preserving the document’s hierarchical structure.
4. Ordinal Ranking Loss – Instead of a plain cross‑entropy loss, the authors introduce a pairwise sorting loss: for any two sentences/documents with different labels, the model is penalized if it predicts them in the wrong order. This encourages the network to respect the natural ordering of readability levels.
5. Training Pipeline – The sentence‑level and document‑level components are trained jointly, allowing gradients from the document loss to refine sentence predictions and vice‑versa.

Results & Findings

• Accuracy gains: On the Chinese dataset, HRNN achieved a 4.2 % absolute improvement over the best baseline; on the English dataset, the gain was 3.7 %.
• Ordinal consistency: The pairwise sorting loss reduced ranking errors by ~15 % compared with a standard cross‑entropy setup, confirming that modeling label order matters.
• Ablation studies showed that removing the sentence‑level supervision dropped document‑level performance by ~2 %, highlighting the benefit of the hierarchical design.
• Qualitative analysis revealed that the model correctly identified “dense” sentences (e.g., heavy technical jargon) as harder, even when the overall document was labeled as moderate, demonstrating nuanced understanding.

Practical Implications

• Content creation tools – Integrated into word processors or CMS platforms, HRNN can give writers real‑time feedback on which paragraphs or sentences need simplification, helping tailor content for specific audiences (e.g., K‑12 education, corporate communications).
• E‑learning & adaptive textbooks – Platforms can automatically grade reading material and dynamically serve texts that match a learner’s proficiency, improving personalization.
• Search & recommendation – Search engines could rank results not just by relevance but also by readability for a given user profile, enhancing accessibility.
• Localization pipelines – Translators can use sentence‑level difficulty scores to prioritize which segments need more careful adaptation when moving content between languages.
• Compliance & legal – Companies can audit policy documents or terms of service to ensure they meet regulatory readability standards (e.g., “plain language” laws).

Limitations & Future Work

• Domain coverage – The experiments focus on news articles and academic abstracts; performance on highly informal text (social media, chat) remains untested.
• Label granularity – The model assumes a fixed set of ordinal levels; extending to a continuous readability score could improve flexibility.
• Resource intensity – Hierarchical transformers can be computationally heavy for very long documents; future work may explore lightweight encoders or sparse attention mechanisms.
• Cross‑lingual transfer – While the approach works for Chinese and English, adapting it to low‑resource languages may require additional multilingual pre‑training or data augmentation strategies.

Overall, the Hierarchical Ranking Neural Network offers a compelling blueprint for next‑generation readability assessment tools that respect both the fine‑grained semantics of sentences and the broader narrative flow of long documents.

Authors

• Yurui Zheng
• Yijun Chen
• Shaohong Zhang

Paper Information

• arXiv ID: 2511.21473v1
• Categories: cs.CL, cs.AI
• Published: November 26, 2025
• PDF: Download PDF