@inproceedings{nguyen-etal-2025-beyond-scientific,
    title = "Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs",
    author = "Nguyen, Quoc-An  and
      Le, Xuan-Hung  and
      Vu, Thi-Minh-Thu  and
      Le, Hoang-Quynh",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.269/",
    doi = "10.18653/v1/2025.findings-emnlp.269",
    pages = "5034--5046",
    ISBN = "979-8-89176-335-7",
    abstract = "Scientific summarization remains a challenging task due to the complex characteristics of internal structure and its external relations to other documents. To address this, our proposed model constructs a heterogeneous graph to represent a document and its relevant external citations. This heterogeneous graph enables the model to exploit information across multiple granularities, ranging from fine-grained textual components to the global document structure, and from internal content to external citation context, which facilitates context-aware representations and effectively reduces redundancy. In addition, we develop an effective encoder based on a multi-granularity graph attention mechanism and the triplet loss objective to enhance representation learning performance. Experimental results across three different scenarios consistently demonstrate that our model outperforms existing approaches. Source code is available at: https://github.com/quocanuetcs/CiteHeteroSum."
}