Automated structuring of medical prescriptions is critical for downstream safety checks in pharmacies, yet remains challenging due to heterogeneous layouts, OCR noise, and dense clinical abbreviations in real-world documents. Existing language models either ignore layout information, rely on computationally expensive image-based architectures, or cannot operate under strict privacy and hardware constraints such as GDPR and HDS-certified environments.We present a lightweight (<10M parameters), privacy-preserving transformer specifically designed for Entity Extraction (EE) and Entity Linking (EL) in French medical prescriptions. The model uses only OCR text and normalized 2D word coordinates, enabling robust pseudonymisation and real-time CPU-level inference while preserving essential spatial cues. It is pretrained on a large corpus of pseudonymised OCR outputs using objectives tailored to prescription structure, including a novel Token-to-Line Alignment (TLA) task, and fine-tuned on the Rx-PAD dataset (Pattin Cottet et al., 2025).Empirical results show that our approach matches or surpasses larger document-understanding models and rivals multimodal LLMs on strict extraction metrics, while achieving sub-second latency suitable for operational deployment. The system is currently used in 230 pharmacies, demonstrating both scalability and practical relevance. These findings highlight the importance of specialized, domain-aware, lightweight models for safe, efficient, and legally compliant prescription verification.

Multi-document summarization (MDS) is a difficult task in Natural Language Processing, aiming to summarize information from several documents. However, the source documents are often insufficient to obtain a qualitative summary. We propose a retriever-guided model combined with non-parametric memory for summary generation. This model retrieves relevant candidates from a database and then generates the summary considering the candidates with a copy mechanism and the source documents. The retriever is implemented with Approximate Nearest Neighbor Search (ANN) to search large databases. Our method is evaluated on the MultiXScience dataset which includes scientific articles. Finally, we discuss our results and possible directions for future work.

The predominant approaches for extracting key information from documents resort to classifiers predicting the information type of each word. However, the word level ground truth used for learning is expensive to obtain since it is not naturally produced by the extraction task. In this paper, we discuss a new method for training extraction models directly from the textual value of information. The extracted information of a document is represented as a sequence of tokens in the XML language. We learn to output this representation with a pointer-generator network that alternately copies the document words carrying information and generates the XML tags delimiting the types of information. The ability of our end-to-end method to retrieve structured information is assessed on a large set of business documents. We show that it performs competitively with a standard word classifier without requiring costly word level supervision.