At the heart of the Pyramid evaluation method for text summarization lie
human written summary content units (SCUs). These SCUs are concise sentences
that decompose a summary into small facts. Such SCUs can be used to judge the
quality of a candidate summary, possibly partially automated via natural
language inference (NLI) systems. Interestingly, with the aim to fully automate
the Pyramid evaluation, Zhang and Bansal (2021) show that SCUs can be
approximated by automatically generated semantic role triplets (STUs). However,
several questions currently lack answers, in particular: i) Are there other
ways of approximating SCUs that can offer advantages? ii) Under which
conditions are SCUs (or their approximations) offering the most value? In this
work, we examine two novel strategies to approximate SCUs: generating SCU
approximations from AMR meaning representations (SMUs) and from large language
models (SGUs), respectively. We find that while STUs and SMUs are competitive,
the best approximation quality is achieved by SGUs. We also show through a
simple sentence-decomposition baseline (SSUs) that SCUs (and their
approximations) offer the most value when ranking short summaries, but may not
help as much when ranking systems or longer summaries.

本研究通过生成语义角色三元组和大型语言模型来近似摘要内容单元（SCUs），探讨了在文本摘要中 SCUs 的使用条件和价值，并发现大型语言模型是实现最佳近似质量的最佳策略。