Work

Towards Robust Synthetic Data Generation for Simplification of Text in French

Year: 2025

Type: article

Abstract: We present a pipeline for synthetic simplification of text in French that combines large language models with structured semantic guidance. Our approach enhances data generation by integrating contextual knowledge from Wikipedia and Vikidia articles and injecting symbolic control through lightweight knowledge graphs. To construct document-level representations, we implement a progressive summarization process that incrementally builds running summaries and extracts key ideas. Simplifications are generated iteratively and assessed using semantic comparisons between input and output graphs, enabling targeted regeneration when critical information is lost. Our system is implemented using LangChain’s orchestration framework, allowing modular and extensible coordination of LLM components. Evaluation shows that context-aware prompting and semantic feedback improve simplification quality across successive iterations.

Source: Machine Learning and Knowledge Extraction

DOI: 10.3390/make7030068

Author: Nikos Tsourakis

Institution: University of Geneva

Language: English

Topic: Text Readability and Simplification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung