Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts
<|reference_start|>Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts: 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.<|reference_end|>
@article{guo2024disordered-dabs:,
    title={Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in
  Disordered Texts},
    author={Xiaobo Guo and Soroush Vosoughi},
    journal={arXiv preprint arXiv:2402.10554},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10554},
    primaryClass={cs.CL}
}