@inproceedings{oshika-sasano-2025-optimizing,
    title = "Optimizing the Arrangement of Citations in Related Work Section",
    author = "Oshika, Masashi  and
      Sasano, Ryohei",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.75/",
    pages = "1362--1373",
    ISBN = "979-8-89176-298-5",
    abstract = "In related work section of a scientific paper, authors collect relevant citations and structure them into coherent paragraphs that follow a logical order. Previous studies have addressed citation recommendation and related work section generation in settings where both the citations and their order are provided in advance. However, they have not adequately addressed the optimal ordering of these citations, which is a critical step for achieving fully automated related work section generation. In this study, we propose a new task, citation arrangement, which focuses on determining the optimal order of cited papers to enable fully automated related work section generation. Our approach decomposes citation arrangement into three tasks: citation clustering, paragraph ordering, and citation ordering within a paragraph. For each task, we propose a method that uses a large language model (LLM) in combination with a graph-based technique to comprehensively consider the context of each paper and the relationships among all cited papers. The experimental results show that our method is more effective than methods that generate outputs for each task using only an LLM."
}