@inproceedings{askari-etal-2025-assessing,
    title = "Assessing {LLM}s for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing",
    author = "Askari, Hadi  and
      Chhabra, Anshuman  and
      Chen, Muhao  and
      Mohapatra, Prasant",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.116/",
    pages = "2187--2201",
    ISBN = "979-8-89176-195-7",
    abstract = "Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot generation of abstractive summaries for given articles. However, little is known about the robustness of such a process of zero-shot summarization.To bridge this gap, we propose *relevance paraphrasing*, a simple strategy that can be used to measure the robustness of LLMs as summarizers. The relevance paraphrasing approach identifies the most *relevant* sentences that contribute to generating an ideal summary, and then *paraphrases* these inputs to obtain a minimally perturbed dataset. Then, by evaluating model performance for summarization on both the original and perturbed datasets, we can assess the LLM{'}s one aspect of robustness. We conduct extensive experiments with relevance paraphrasing on 4 diverse datasets, as well as 4 LLMs of different sizes (GPT-3.5-Turbo, Llama-2-13B, Mistral-7B-v1, and Dolly-v2-7B). Our results indicate that LLMs are not consistent summarizers for the minimally perturbed articles, necessitating further improvements."
}