Multi-document summarization (MDS) has traditionally been studied assuming a
set of ground-truth topic-related input documents is provided. In practice, the
input document set is unlikely to be available a priori and would need to be
retrieved based on an information need, a setting we call open-domain MDS. We
experiment with current state-of-the-art retrieval and summarization models on
several popular MDS datasets extended to the open-domain setting. We find that
existing summarizers suffer large reductions in performance when applied as-is
to this more realistic task, though training summarizers with retrieved inputs
can reduce their sensitivity retrieval errors. To further probe these findings,
we conduct perturbation experiments on summarizer inputs to study the impact of
different types of document retrieval errors. Based on our results, we provide
practical guidelines to help facilitate a shift to open-domain MDS. We release
our code and experimental results alongside all data or model artifacts created
during our investigation.

本文研究了 Multi-document summarization (MDS) 在基于开放领域的信息检索下的应用。作者在实验中发现现有的摘要算法性能较低，但通过训练摘要算法可以减少检索错误对算法性能的影响。此外，作者还进行了扰动实验研究了不同类型的文档检索错误对算法影响并提出了实用指南。