Long-form question answering systems provide rich information by presenting paragraph-level answers, often containing optional background or auxiliary information. While such comprehensive answers are helpful, not all information is required to answer the question (e.g. users with domain knowledge do not need an explanation of background). Can we provide a concise version of the answer by summarizing it, while still addressing the question? We conduct a user study on summarized answers generated from state-of-the-art models and our newly proposed extract-and-decontextualize approach. We find a large proportion of long-form answers (over 90%) in the ELI5 domain can be adequately summarized by at least one system, while complex and implicit answers are challenging to compress. We observe that decontextualization improves the quality of the extractive summary, exemplifying its potential in the summarization task. To promote future work, we provide an extractive summarization dataset covering 1K long-form answers and our user study annotations. Together, we present the first study on summarizing long-form answers, taking a step forward for QA agents that can provide answers at multiple granularities.

翻译：长篇问答系统通过提供段落级答案，常包含可选的背景或辅助信息，从而传递丰富的信息。尽管此类全面答案有所帮助，但并非所有信息都是回答问题的必要内容（例如，具备领域知识的用户无需背景解释）。能否通过总结答案来提供简洁版本，同时仍能回应问题？我们针对基于最先进模型及我们新提出的"提取-去语境化"方法生成的总结答案开展了一项用户研究。研究发现，在ELI5领域中，超过90%的长篇答案至少能被一种系统充分总结，但复杂且隐晦的答案难以压缩。我们观察到，去语境化能提升抽取式摘要的质量，展现了其在总结任务中的潜力。为促进后续研究，我们提供了一个涵盖1000条长篇答案的抽取式摘要数据集及用户研究标注。综上，我们首次对长篇答案的总结问题展开研究，推动问答智能体向能够提供多粒度答案的方向迈进一步。