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.- Anthology ID:
- 2023.inlg-genchal.18
- Volume:
- Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges
- Month:
- September
- Year:
- 2023
- Address:
- Prague, Czechia
- Editor:
- Simon Mille
- Venues:
- INLG | SIGDIAL
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 132–137
- Language:
- URL:
- https://aclanthology.org/2023.inlg-genchal.18
- DOI:
- Cite (ACL):
- Eugene Borisov and Nikolay Mikhaylovskiy. 2023. Team NTR @ AutoMin 2023: Dolly LLM Improves Minuting Performance, Semantic Segmentation Doesn’t. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges, pages 132–137, Prague, Czechia. Association for Computational Linguistics.
- Cite (Informal):
- Team NTR @ AutoMin 2023: Dolly LLM Improves Minuting Performance, Semantic Segmentation Doesn’t (Borisov & Mikhaylovskiy, INLG-SIGDIAL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.inlg-genchal.18.pdf