Concise Answers to Complex Questions: Summarization of Long-form Answers
Abhilash Potluri Fangyuan Xu Eunsol Choi
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.