Progressive Document-level Text Simplification via Large Language Models (2501.03857v1)

Abstract: Research on text simplification has primarily focused on lexical and sentence-level changes. Long document-level simplification (DS) is still relatively unexplored. LLMs, like ChatGPT, have excelled in many natural language processing tasks. However, their performance on DS tasks is unsatisfactory, as they often treat DS as merely document summarization. For the DS task, the generated long sequences not only must maintain consistency with the original document throughout, but complete moderate simplification operations encompassing discourses, sentences, and word-level simplifications. Human editors employ a hierarchical complexity simplification strategy to simplify documents. This study delves into simulating this strategy through the utilization of a multi-stage collaboration using LLMs. We propose a progressive simplification method (ProgDS) by hierarchically decomposing the task, including the discourse-level, topic-level, and lexical-level simplification. Experimental results demonstrate that ProgDS significantly outperforms existing smaller models or direct prompting with LLMs, advancing the state-of-the-art in the document simplification task.

• The paper introduces ProgDS, a novel multi-stage LLM-driven method for document-level simplification by progressively addressing discourse, topic, and lexical elements.
• It leverages iterative collaboration among LLMs to maintain document coherence while significantly boosting evaluation metrics like SARI and FKGL.
• Empirical results on Wiki-auto and Newsela datasets demonstrate that ProgDS outperforms baselines, indicating its potential for real-world applications.

Progressive Document-level Text Simplification via LLMs

The paper "Progressive Document-level Text Simplification via LLMs" introduces a novel methodology aimed at addressing the shortcomings of current LLMs, such as ChatGPT, in the domain of document-level text simplification (DS). The traditional focus of text simplification research has been on sentence and lexical-level transformations, largely ignoring the complexities and requirements of longer documents. This paper proposes a progressive method, ProgDS, which integrates discourse, topic, and lexical simplifications in a multi-stage collaboration process, thereby refining and enhancing document coherence and readability.

Key Contributions

1. Progressive Simplification Approach: The authors introduce a hierarchical and decomposed approach to DS, encompassing discourse-level, topic-level, and lexical-level simplifications. The method starts from the overall structure, progresses through paragraph and sentence arrangements, and finally addresses word-level expressions, effectively mimicking human editors' strategies.
2. Multi-stage Collaboration with LLMs: The paper leverages LLMs in a multi-stage setting, where each simplification layer acts sequentially and iteratively to ensure the final output maintains logical integrity and simplicity without substantial loss of original content.
3. Enhanced Simplification Metrics: ProgDS is empirically demonstrated to outperform existing techniques, providing significant improvements in standard evaluation metrics like SARI, D-SARI, and FKGL on datasets including Wiki-auto and Newsela.

Numerical Results and Analysis

The paper reports that the ProgDS method notably outperforms various baseline models such as BART-SWI and PG$_{\text{Dyn}$ in terms of simplification accuracy and document coherence. For instance, ProgDS achieved a SARI score of 45.83 on the Wiki-auto dataset, indicating a substantial advancement in processing longer text entries while preserving essential information. Furthermore, the implementation of iterative simplification further optimizes the output, suggesting that iterative refinement can enhance the comprehensibility and accessibility of the text.

Implications and Future Prospects

ProgDS bridges a crucial gap between advanced LLM capabilities and real-world applications by addressing DS challenges such as content preservation and subjective ambiguity in text interpretation. It provides a significant step towards utilizing LLMs in editing tasks that demand high-level contextual understanding and document-wide coherence maintenance. The findings imply potential applications not only in DS but also broadly in long document processing tasks. As LLM capabilities evolve, integrating such structured hierarchical modeling could further refine results, offering synergy with emerging AI editing and summarization systems.

Speculation on AI Advancement

The paper underscores the latent capabilities of LLMs when strategically prompted and structured. Moving forward, enhancements in instruction-tuning and iterative learning paradigms could make LLMs adept at performing more complex editing tasks autonomously. Furthermore, by continuously evolving the architecture of LLMs to accommodate better contextual understanding, we can anticipate more intelligent systems capable of autonomously adapting documents not only for simplification but also for other nuanced transformations.

In conclusion, the paper presents a technically sound and well-researched approach to DS via LLMs, overcoming previous limitations and setting the groundwork for future explorations in AI-driven text processing. The progressive simplification method not only improves the utility of LLMs in DS but also provides a valuable framework that can be extrapolated to other AI applications in document comprehension and simplification.