: 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[...]
: Regulatory Impact Assessment is an essential process for evaluating policy feasibility and balancing stakeholder interests prior to enactment. However, manual analysis is often time-consuming and error-prone due to the unstructured nature of legal documents and complex cross-referencing networks. To address this challenge, we propose a framework that automates legal information extraction and multi-label impact classification by integrating a Large Language Model (LLM) with ontologybased reasoning. Initially, an LLM utilizes few-shot prompt engineering to extract structured entities (agents, actions, states) and primary legal signals (deontic words, impacted targets) from raw texts. Subsequently, this extracted data is mapped into a domain-specific ontology, where a HermiT reasoner applies formal Description Logic axioms to automatically classify impacts into qualitative or quantitative benefits, costs, and constraints. Experimental evaluations on a curated dataset demonstrate the framework's effectiveness, [...]