Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913393810604032 |
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| author | Guo, Xiaobo Vosoughi, Soroush |
| author_facet | Guo, Xiaobo Vosoughi, Soroush |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_10554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts Guo, Xiaobo Vosoughi, Soroush Computation and Language 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. |
| title | Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.10554 |