@inproceedings{liang-etal-2024-distantly,
    title = "Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization",
    author = "Liang, Junzhe  and
      Sun, Haifeng  and
      Zhuang, Zirui  and
      Qi, Qi  and
      Wang, Jingyu  and
      Liao, Jianxin",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.448",
    pages = "5006--5017",
    abstract = "Code summarization provides a natural language description for a given piece of code. In this work, we focus on scripting code{---}programming languages that interact with specific devices through commands. The low-resource nature of scripting languages makes traditional code summarization methods challenging to apply. To address this, we introduce a novel framework: distantly supervised contrastive learning for low-resource scripting language summarization. This framework leverages limited atomic commands and category constraints to enhance code representations. Extensive experiments demonstrate our method{'}s superiority over competitive baselines.",
}

Code summarization provides a natural language description for a given piece of code. In this work, we focus on scripting code{---}programming languages that interact with specific devices through commands. The low-resource nature of scripting languages makes traditional code summarization methods challenging to apply. To address this, we introduce a novel framework: distantly supervised contrastive learning for low-resource scripting language summarization. This framework leverages limited atomic commands and category constraints to enhance code representations. Extensive experiments demonstrate our method{'}s superiority over competitive baselines.
Liang, Junzhe Sun, Haifeng Zhuang, Zirui Qi, Qi Wang, Jingyu Liao, Jianxin
Distantly Supervised Contrastive Learning for Low-Resource Scripting Language Summarization