@inproceedings{guo-vosoughi-2024-disordered,
title = "Disordered-{DABS}: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts",
author = "Guo, Xiaobo and
Vosoughi, Soroush",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.24",
pages = "416--431",
abstract = "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.",
}
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%0 Conference Proceedings
%T Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts
%A Guo, Xiaobo
%A Vosoughi, Soroush
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F guo-vosoughi-2024-disordered
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.24
%P 416-431
Markdown (Informal)
[Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts](https://aclanthology.org/2024.findings-emnlp.24) (Guo & Vosoughi, Findings 2024)
ACL