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Modeling Perspectives in NLP: Parameter-Efficient Perspective Conditioning for Span Extraction and Summarization

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.