@inproceedings{hu-etal-2024-reduce,
    title = "Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework",
    author = "Hu, Xiaoyu  and
      Zhang, Xu  and
      Lin, Zexu  and
      Zhou, Deyu",
    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.1198",
    pages = "13722--13733",
    abstract = "Code summarization is the task of automatically generating natural language descriptions from source code. Recently, pre-trained language models have gained significant popularity in code summarization due to their capacity to capture richer semantic representations of both code and natural language. Nonetheless, contemporary code summarization models grapple with two fundamental limitations. (1) Some tokens in the code are irrelevant to the natural language description and damage the alignment of the representation spaces for code and language. (2) Most approaches are based on the encoder-decoder framework, which is often plagued by the exposure bias problem, hampering the effectiveness of their decoding sampling strategies. To address the two challenges, we propose a novel pipeline framework named Reduce Redundancy then Rerank (Re{\^{}}3). Specifically, a redundancy reduction component is introduced to eliminate redundant information in code representation space. Moreover, a re-ranking model is incorporated to select more suitable summary candidates, alleviating the exposure bias problem. The experimental results show the effectiveness of Re{\^{}}3 over some state-of-the-art approaches across six different datasets from the CodeSearchNet benchmark.",
}

Code summarization is the task of automatically generating natural language descriptions from source code. Recently, pre-trained language models have gained significant popularity in code summarization due to their capacity to capture richer semantic representations of both code and natural language. Nonetheless, contemporary code summarization models grapple with two fundamental limitations. (1) Some tokens in the code are irrelevant to the natural language description and damage the alignment of the representation spaces for code and language. (2) Most approaches are based on the encoder-decoder framework, which is often plagued by the exposure bias problem, hampering the effectiveness of their decoding sampling strategies. To address the two challenges, we propose a novel pipeline framework named Reduce Redundancy then Rerank (Re{\^{}}3). Specifically, a redundancy reduction component is introduced to eliminate redundant information in code representation space. Moreover, a re-ranking model is incorporated to select more suitable summary candidates, alleviating the exposure bias problem. The experimental results show the effectiveness of Re{\^{}}3 over some state-of-the-art approaches across six different datasets from the CodeSearchNet benchmark.
Hu, Xiaoyu Zhang, Xu Lin, Zexu Zhou, Deyu
Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework