Abstract
Automatically generating meeting minutes is a challenging yet time-relevant problem in speech and natural language processing. Nowadays, meeting minutes seem more cru- cial than ever due to the manifold rise of on- line meetings.
However, automatic minut- ing is not straightforward for various reasons: obtaining transcriptions of sufficient quality, summarizing long dialogue discourse, retain- ing topical relevance and coverage, handling redundancies and small talk, etc. This pa- per presents our investigations on a pipelined approach to automatically generate meeting minutes using a BART model (Bidirectional and Auto-Regressive Transformers) trained on multi-party dialogue summarization datasets.
We achieve comparable results with our sim- ple yet intuitive method with respect to pre- vious large and computationally heavy state- of-the-art models. We make our code avail- able at https://github.com/ELITR/ minuting-pipeline.