@inproceedings{schneider-turchi-2023-team,
    title = "Team Zoom @ {A}uto{M}in 2023: Utilizing Topic Segmentation And {LLM} Data Augmentation For Long-Form Meeting Summarization",
    author = "Schneider, Felix  and
      Turchi, Marco",
    editor = "Mille, Simon",
    booktitle = "Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges",
    month = sep,
    year = "2023",
    address = "Prague, Czechia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.inlg-genchal.14/",
    pages = "101--107",
    abstract = "This paper describes Zoom{'}s submission to the Second Shared Task on Automatic Minuting at INLG 2023. We participated in Task A: generating abstractive summaries of meetings. Our final submission was a transformer model utilizing data from a similar domain and data augmentation by large language models, as well as content-based segmentation. The model produces summaries covering meeting topics and next steps and performs comparably to a large language model at a fraction of the cost. We also find that re-summarizing the summaries with the same model allows for an alternative, shorter summary."
}