  Community-based Question Answering (CQA), which allows users to acquire their
desired information, has increasingly become an essential component of online
services in various domains such as E-commerce, travel, and dining. However, an
overwhelming number of CQA pairs makes it difficult for users without
particular intent to find useful information spread over CQA pairs. To help
users quickly digest the key information, we propose the novel CQA
summarization task that aims to create a concise summary from CQA pairs. To
this end, we first design a multi-stage data annotation process and create a
benchmark dataset, CoQASUM, based on the Amazon QA corpus. We then compare a
collection of extractive and abstractive summarization methods and establish a
strong baseline approach DedupLED for the CQA summarization task. Our
experiment further confirms two key challenges, sentence-type transfer and
deduplication removal, towards the CQA summarization task. Our data and code
are publicly available.
