Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot summarization of abstractive summaries for given articles, but little is known about their robustness at this task. |
| Approach: | They propose a strategy that uses the most relevant sentences to generate an ideal summary and then paraphrases them to obtain a minimally perturbed dataset. |
| Outcome: | The proposed approach can be used to measure the robustness of LLMs as summarizers on a minimally perturbed dataset. |
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