@inproceedings{sinha-2025-qa,
    title = "{QA}-prompting: Improving Summarization with Large Language Models using Question-Answering",
    author = "Sinha, Neelabh",
    editor = "Dong, Yue  and
      Xiao, Wen  and
      Zhang, Haopeng  and
      Zhang, Rui  and
      Ernst, Ori  and
      Wang, Lu  and
      Liu, Fei",
    booktitle = "Proceedings of The 5th New Frontiers in Summarization Workshop",
    month = nov,
    year = "2025",
    address = "Hybrid",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.newsum-main.14/",
    pages = "199--212",
    ISBN = "979-8-89176-337-1",
    abstract = "Language Models (LMs) have revolutionized natural language processing, enabling high-quality text generation through prompting and in-context learning. However, models often struggle with long-context summarization due to positional biases, leading to suboptimal extraction of critical information. There are techniques to improve this with fine-tuning, pipelining, or using complex techniques, which have their own challenges. To solve these challenges, we propose QA-prompting - a simple prompting method for summarization that utilizes question-answering as an intermediate step prior to summary generation. Our method extracts key information and enriches the context of text to mitigate positional biases and improve summarization in a single LM call per task without requiring fine-tuning or pipelining. Experiments on multiple datasets belonging to different domains using ten state-of-the-art pre-trained models demonstrate that QA-prompting outperforms baseline and other state-of-the-art methods, achieving up to 29{\%} improvement in ROUGE scores. This provides an effective and scalable solution for summarization and highlights the importance of domain-specific question selection for optimal performance."
}