Abstractive Question Answering (AQA) is a pivotal task in Natural Language Processing (NLP) that requires AI models to not only comprehend the context and the question but also generate accurate, clear, and comprehensive answers. This task holds particular significance in the medical field, where AI-driven question answering has numerous applications, such as assisting with patient queries, supporting clinical decision-making, and improving access to medical information. In particular, pregnancy health is a critical area within health care AI, where there is increasing focus on leveraging AI to enhance outcomes in pregnancy-related care. However, medical question answering presents unique challenges due to the complexity of symptoms, which are often ambiguous or overlapping across different health conditions. Thus, the development of high-quality datasets is crucial for building and evaluating healthcare AI systems. In this paper, we introduce a closed-domain abstractive question answering dataset in Vietnamese, specifically focused on pregnancy health. This dataset is designed to address the distinct challenges of medical QA in a low-resource language and aims to support further research and innovation in healthcare AI, particularly in the context of pregnancy-related care.