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.

本研究提出了一种透视特定的回答总结的新任务，通过识别医疗领域不同的观点并进行总结，以应对医疗社区问答平台中回答多样性和离题讨论带来的信息提取困难。研究使用 PUMA 数据集对 3167 个问答主题进行 6193 个透视感知的总结标注，提出了 PLASMA，一种基于提示的可控总结模型，并通过与其他基线模型的评估结果证明了 PLASMA 的卓越性能。

无视角无感知！透视感知式医疗问答摘要

No perspective, no perception!! Perspective-aware Healthcare Answer  Summarization

Community Question Answering (CQA) fora such as Stack Overflow and Yahoo!
Answers contain a rich resource of answers to a wide range of community-based
questions. Each question thread can receive a large number of answers with
different perspectives. One goal of answer summarization is to produce a
summary that reflects the range of answer perspectives. A major obstacle for
this task is the absence of a dataset to provide supervision for producing such
summaries. Recent works propose heuristics to create such data, but these are
often noisy and do not cover all answer perspectives present. This work
introduces a novel dataset of 4,631 CQA threads for answer summarization
curated by professional linguists. Our pipeline gathers annotations for all
subtasks of answer summarization, including relevant answer sentence selection,
grouping these sentences based on perspectives, summarizing each perspective,
and producing an overall summary. We analyze and benchmark state-of-the-art
models on these subtasks and introduce a novel unsupervised approach for
multi-perspective data augmentation that boosts summarization performance
according to automatic evaluation. Finally, we propose reinforcement learning
rewards to improve factual consistency and answer coverage and analyze areas
for improvement.

该研究使用机器学习和数据归纳技术，为 Community Question Answering (CQA) fora 提出了一种新的答案总结方法，以产生最能反映回答视角范围的摘要，这种方法使用了由专业语言学家策划的数据集进行训练，并实现了语句选取、分组、摘要和纠正等多个子任务，使用了强化学习技术及数据增强方法提高其性能。