Disordered-DABS：面向无序文本的动态基于方面摘要基准
Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts
AI总结：
针对非结构化、动态场景下基于方面摘要研究的不足，论文通过改造现有数据集构建 Disordered-DABS 基准，并经实验与人工评估证明其对 GPT-3.5 等现代摘要模型构成新挑战。
摘要：
基于方面的摘要已取得显著进展，尤其是在结构化文本方面。然而，对社交媒体和客户反馈中常见的大规模无序文本进行摘要，仍是一项重大挑战。当前研究主要面向结构化文本中的预定义方面，忽视了动态且无序环境的复杂性。为弥补这一空白，我们提出 Disordered-DABS，这是一个专为非结构化文本定制的新型动态基于方面摘要基准。该基准通过改造现有数据集开发，以兼顾成本效率和可扩展性；我们的全面实验和细致人工评估表明，Disordered-DABS 给当代摘要模型（包括 GPT-3.5 等最先进语言模型）带来了独特挑战。
英文摘要：
Aspect-based summarization has seen significant advancements, especially in structured text. Yet, summarizing disordered, large-scale texts, like those found in social media and customer feedback, remains a significant challenge. Current research largely targets predefined aspects within structured texts, neglecting the complexities of dynamic and disordered environments. Addressing this gap, we introduce Disordered-DABS, a novel benchmark for dynamic aspect-based summarization tailored to unstructured text. Developed by adapting existing datasets for cost-efficiency and scalability, our comprehensive experiments and detailed human evaluations reveal that Disordered-DABS poses unique challenges to contemporary summarization models, including state-of-the-art language models such as GPT-3.5.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
