Understanding text through multiple perspectives is essential in domains such as healthcare community question answering, where answers frequently contain heterogeneous viewpoints, including experiences, suggestions, causes, follow-up questions, and informational claims. We present a unified perspective-conditioned framework for both span identification and perspective-aware summarization on the PerAnsSumm dataset. Our approach introduces explicit perspective signals into transformer models using two parameter-efficient mechanisms: prefix-conditioned representations and perspective-aware attention layers. We first employ multi-label perspective classification to identify relevant viewpoints, which serve as conditioning signals for downstream tasks. For span identification, we model perspective-specific extraction as a conditioned binary sequence labeling problem. For summarization, we guide generation using perspective-enriched encoder representations. Experiments demonstrate that explicit perspective conditioning substantially improves span detection performance while achieving competitive summarization quality. Notably, perspective-aware attention achieves strong results using only a small fraction of the trainable parameters required by full fine-tuning. Our findings highlight the importance of structured viewpoint modeling and show that explicit perspective control enables efficient and interpretable multi-perspective text understanding.