@inproceedings{chen-etal-2022-dialogsum,
    title = "{D}ialog{S}um Challenge: Results of the Dialogue Summarization Shared Task",
    author = "Chen, Yulong  and
      Deng, Naihao  and
      Liu, Yang  and
      Zhang, Yue",
    editor = "Shaikh, Samira  and
      Ferreira, Thiago  and
      Stent, Amanda",
    booktitle = "Proceedings of the 15th International Conference on Natural Language Generation: Generation Challenges",
    month = jul,
    year = "2022",
    address = "Waterville, Maine, USA and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.inlg-genchal.14/",
    pages = "94--103",
    abstract = "We report the results of DialogSum Challenge, the shared task on summarizing real-life sce- nario dialogues at INLG 2022. Four teams participate in this shared task and three submit their system reports, exploring different meth- ods to improve the performance of dialogue summarization. Although there is a great im- provement over the baseline models regarding automatic evaluation metrics, such as ROUGE scores, we find that there is a salient gap be- tween model generated outputs and human an- notated summaries by human evaluation from multiple aspects. These findings demonstrate the difficulty of dialogue summarization and suggest that more fine-grained evaluatuion met- rics are in need."
}