  Healthcare Community Question Answering (CQA) forums offer an accessible
platform for individuals seeking information on various healthcare-related
topics. People find such platforms suitable for self-disclosure, seeking
medical opinions, finding simplified explanations for their medical conditions,
and answering others' questions. However, answers on these forums are typically
diverse and prone to off-topic discussions. It can be challenging for readers
to sift through numerous answers and extract meaningful insights, making answer
summarization a crucial task for CQA forums. While several efforts have been
made to summarize the community answers, most of them are limited to the open
domain and overlook the different perspectives offered by these answers. To
address this problem, this paper proposes a novel task of perspective-specific
answer summarization. We identify various perspectives, within
healthcare-related responses and frame a perspective-driven abstractive summary
covering all responses. To achieve this, we annotate 3167 CQA threads with 6193
perspective-aware summaries in our PUMA dataset. Further, we propose PLASMA, a
prompt-driven controllable summarization model. To encapsulate the
perspective-specific conditions, we design an energy-controlled loss function
for the optimization. We also leverage the prefix tuner to learn the
intricacies of the health-care perspective summarization. Our evaluation
against five baselines suggests the superior performance of PLASMA by a margin
of 1.5-21% improvement. We supplement our experiments with ablation and
qualitative analysis.
