: Maintenance regulations are complex legal texts that are difficult to exploit when addressing a specific case and challenging to integrate into operational systems. This paper presents a two-stage LLM-assisted workflow for French maintenance regulations: ontology engineering from a SEMLEG-based core ontology, followed by construction of an ontology-grounded French legal knowledge graph. The first stage consists in the open extraction of typed entities and triples from a stratified corpus sample, the normalization of labels through embedding-based fusion, and the induction of candidate object properties with their signature (domain and range). The second stage uses the resulting ontology to guide the closed extraction of triples and RDF graph construction over the full corpus. Experiments with GPT-4.1 and mistral-large-2512 show robust structured outputs, near-complete class alignment, and a substantial reduction of duplicated entities and predicates after fusion. Fewer than 20% of triples introduce unseen pr[...]
: Parliamentary institutions generate extensive, domain-specific legislative documents, including normative texts and parliamentary debate transcripts. These documents differ in content and linguistic complexity, making automatic summarization essential for producing coherent summaries aligned with institutional standards. While large language models (LLMs) achieve high summarization quality, their computational requirements limit deployment in parliamentary and public-sector environments. In contrast, small language models (SLMs) offer a more resource-efficient alternative, but their capabilities and performance relative to LLMs, extractive methods, and other SLMs remain underexplored. In this work, we present the first comprehensive evaluation of SLMs for legislative summarization, assessing their effectiveness across document types and languages. We use two complementary datasets: EUR-Lex-Sum, a multilingual corpus of normative texts covering six European languages, and ParcanDeb-Sum, a Spanish dataset of p[...]