  Community Question Answering (CQA) forums such as Stack Overflow and Yahoo!
Answers contain a rich resource of answers to a wide range of questions. Each
question thread can receive a large number of answers with different
perspectives. The goal of multi-perspective answer summarization is to produce
a summary that includes all perspectives of the answer. A major obstacle for
multi-perspective, abstractive answer summarization is the absence of a dataset
to provide supervision for producing such summaries. This work introduces a
novel dataset creation method to automatically create multi-perspective,
bullet-point abstractive summaries from an existing CQA forum. Supervision
provided by this dataset trains models to inherently produce multi-perspective
summaries. Additionally, to train models to output more diverse, faithful
answer summaries while retaining multiple perspectives, we propose a
multi-reward optimization technique coupled with a sentence-relevance
prediction multi-task loss. Our methods demonstrate improved coverage of
perspectives and faithfulness as measured by automatic and human evaluations
compared to a strong baseline.
