  Faceted summarization provides briefings of a document from different
perspectives. Readers can quickly comprehend the main points of a long document
with the help of a structured outline. However, little research has been
conducted on this subject, partially due to the lack of large-scale faceted
summarization datasets. In this study, we present FacetSum, a faceted
summarization benchmark built on Emerald journal articles, covering a diverse
range of domains. Different from traditional document-summary pairs, FacetSum
provides multiple summaries, each targeted at specific sections of a long
document, including the purpose, method, findings, and value. Analyses and
empirical results on our dataset reveal the importance of bringing structure
into summaries. We believe FacetSum will spur further advances in summarization
research and foster the development of NLP systems that can leverage the
structured information in both long texts and summaries.
