: 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[...]