Taking minutes is an essential component of every meeting, although the goals, style, and procedure of this activity ("minuting" for short) can vary. Minuting is a relatively unstructured writing act and is affected by who takes the minutes and for whom the minutes are intended.
With the rise of online meetings, automatic minuting would be an important use-case for the meeting participants and those who might have missed the meeting. However, automatically generating meeting minutes is a challenging problem due to various factors, including the quality of automatic speech recognition (ASR), public availability of meeting data, subjective knowledge of the minuter, etc.
In this work, we present the first of its kind dataset on Automatic Minuting. We develop a dataset of English and Czech technical project meetings, consisting of transcripts generated from ASRs, manually corrected, and minuted by several annotators.
Our dataset, ELITR Minuting Corpus, consists of 120 English and 59 Czech meetings, coveri