Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

TL;DR

This paper introduces FacetSum, a large-scale dataset for faceted summarization of long scientific documents, enabling structured summaries from multiple perspectives to improve comprehension and NLP system development.

Contribution

The creation of FacetSum, a novel dataset with multi-perspective summaries for long scientific articles, addressing the lack of structured summarization resources.

Findings

Structured summaries improve comprehension of long documents.

Empirical results highlight the importance of incorporating structure.

FacetSum facilitates future research in faceted summarization.

Abstract

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.