Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021)

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.

## introduction text summarization is the task of condensing a long piece of text into a short summary without losing salient information. research has shown that a well-structured summary can effectively facilitate comprehension @xcite . a case in point is the structured abstract, which consists of multiple segments, each focusing on a specific facet of a scientific publication @xcite , such as background, method, conclusions, etc. the structure therein can provide much additional clarity for improved comprehension and has long been adopted by databases and publishers such as medline and emerald. despite these evident benefits of structure, summaries are often framed as a linear, structure-less sequence of sentences in the flourishing array of summarization studies @xcite emotion in enterprise social media systems ## experiments and results we use facetsum to benchmark a variety of summarization models from state-of-the-art supervised models to unsupervised and heuristics-based models. we also provide the scores of a sentence-level extractive oracle system @xcite . we report rouge-l in this section and include rouge-1/2 results in appendix e. unsupervised models vs heuristics we report performances of unsupervised and heuristics summarization methods (see empirically, supervised models outperform unsupervised baselines by a large margin ( with i+c as source text, both training strategies exhibit much better results than using full paper. this is opposite to the observation in ## conclusion & future work we introduce facetsum to support the research of faceted summarization, which targets summarizing scientific documents from multiple facets. we provide extensive analyses and results to investigate the characteristics of facetsum. our observations call for the development of models capable of handling very long documents and outputting controlled text. specifically, we will consider exploring the following topics in future work: (1) incorporating methods for long-document processing, such as reducing input length by extracting key sentences @xcite or segments @xcite ; (2) examining the possibility of building a benchmark for systematic generalization @xcite with the domain categories; (3) automatically structuring traditional abstracts (huang et al., 2020) with facetsum. introduction intro, purpose method design, method, approach result result, find, discuss, analy conclusion conclu, future