ELITR Minuting Corpus: A Novel Dataset for Automatic Minuting from Multi-Party Meetings in English and Czech
15435Authors
nan
Abstract
AbstractTaking 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 rather unstructured writing activity and is affected by who is taking the minutes and for whom the intended minutes are. With the rise of online meetings, automatic minuting would be an important benefit for the meeting participants as well as for those who might have missed the meeting. However, automatically generating meeting minutes is a challenging problem due to a variety of factors including the quality of automatic speech recorders (ASRs), availability of public 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 which consists of transcripts generated from ASRs, manually corrected, and minuted by several annotators. Our dataset, AutoMin, consists of 113 (English) and 53 (Czech) meetings, covering more than 160 hours of meeting content. Upon acceptance, we will publicly release (aaa.bbb.ccc) the dataset as a set of meeting transcripts and minutes, excluding the recordings for privacy reasons. A unique feature of our dataset is that most meetings are equipped with more than one minute, each created independently. Our corpus thus allows studying differences in what people find important while taking the minutes. We also provide baseline experiments for the community to explore this novel problem further. To the best of our knowledge AutoMin is probably the first resource on minuting in English and also in a language other than English (Czech).
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Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 3174–3182 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 3174 ELITR Minuting Corpus: A Novel Dataset for Automatic Minuting from Multi-Party Meetings in English and Czech Anna Nedoluzhko, Muskaan Singh, Marie Hledíková, Tirthankar Ghosal, Ondˇrej Bojar Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Prague (nedoluzhko,singh,hledikova,ghosal,bojar)@ufal.mff.cuni.cz Abstract 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, covering about 180 hours of meeting content. The corpus is publicly available at http://hdl.handle.net/11234/1-4692 as a set of meeting transcripts and minutes, excluding the recordings for privacy reasons. A unique feature of our dataset is that most meetings are equipped with more than one minute, each created independently. Our corpus thus allows studying differences in what people find important while taking the minutes. We also provide baseline experiments for the community to explore this novel problem further. To the best of our knowledge, ELITR Minuting Corpus is probably the first resource on minuting in English and also in a language other than English (Czech). Keywords: automatic minuting, meeting summarization, multi-party dialogues 1. Introduction A significant portion of the working population has their mainstream interaction and meetings virtual these days. Amongst many other things, the COVID-19 pandemic has led people to discover innovative ways to continue their work and adapt to the “new normal”. Hence virtual meetings are now an integral part of life for the working population. As one has to attend more and more meetings, it requires a considerable effort to note down and retrieve the desired information from the meeting as and when required. Fre- quent meetings and the necessary context switching give rise to undesired information overload on the participants. For this, usually, there is a designated participant or a scribe who jots down the minutes of the meeting (see Figure 1), which can consist of essential issues, action points, deci- sions, or proposed activities discussed during the meeting. Manually writing minutes takes time and distracts attention from the discussion. Hence we believe that an automatic minuting solution will be a practical application of …
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Labeled Text
AbstractTaking 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 rather unstructured writing activity and is affected by who is taking the minutes and for whom the intended minutes are. With the rise of online meetings, automatic minuting would be an important benefit for the meeting participants as well as for those who might have missed the meeting. However, automatically generating meeting minutes is a challenging problem due to a variety of factors including the quality of automatic speech recorders (ASRs), availability of public 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 which consists of transcripts generated from ASRs, manually corrected, and minuted by several annotators. Our dataset, AutoMin, consists of 113 (English) and 53 (Czech) meetings, covering more than 160 hours of meeting content. Upon acceptance, we will publicly release (aaa.bbb.ccc) the dataset as a set of meeting transcripts and minutes, excluding the recordings for privacy reasons. A unique feature of our dataset is that most meetings are equipped with more than one minute, each created independently. Our corpus thus allows studying differences in what people find important while taking the minutes. We also provide baseline experiments for the community to explore this novel problem further. To the best of our knowledge AutoMin is probably the first resource on minuting in English and also in a language other than English (Czech).
Publication Details
- Paper ID:
- 15435
- Year:
- 2022
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan