@inproceedings{borisov-mikhaylovskiy-2023-team,
    title = "Team {NTR} @ {A}uto{M}in 2023: Dolly {LLM} Improves Minuting Performance, Semantic Segmentation Doesn`t",
    author = "Borisov, Eugene  and
      Mikhaylovskiy, Nikolay",
    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/jlcl-multiple-ingestion/2023.inlg-genchal.18/",
    pages = "132--137",
    abstract = "This paper documents the approach of Team NTR for the Second Shared Task on Automatic Minuting (AutoMin) at INLG 2023. The goal of this work is to develop a module for automatic generation of meeting minutes based on a meeting transcript text produced by an Automated Speech Recognition (ASR) system (Task A). We consider minuting as a supervised machine learning task on pairs of texts: the transcript of the meeting and its minutes. We use a two-staged minuting pipeline that consists of segmentation and summarization. We experiment with semantic segmentation and multi-language approaches and Large Language Model Dolly, and achieve Rouge1-F of 0.2455 and BERT-Score of 0.8063 on the English part of ELITR test set and Rouge1-F of 0.2430 and BERT-Score of 0.8332 on the EuroParl dev set with the submitted Naive Segmentation + Dolly7b pipeline."
}