Purpose
The purpose of this paper is to describe a new supervised machine learning study on the prediction of meeting participant’s personal note-taking from spoken dialogue acts uttered shortly before writing.
Design/methodology/approach
This novel approach of providing cues for finding important meeting events that would be worth recording in a meeting summary looks at temporal overlaps of multiple people’s note-taking. This research uses data of 124 meetings taken from the AMI meeting corpus.
Findings
The results show that several machine learning methods that the authors compared were able to classify the data significantly better than a random approach. The best model, decision trees with feature selection, achieved 70 per cent accuracy for the binary distinction writing for any number of participants simultaneously or no writing, whereas the performance for a more fine-grained distinction of the number of participants taking notes showed only about 30 per cent accuracy.
Research limitations/implications
The findings suggest that meeting participants take personal notes in accordance with the utterance of previously uttered speech acts, particularly dialogue acts about disfluencies and assessments appear to influence the note-taking activities. However, further research is necessary to examine other domains and to determine in what way this behaviour is helpful as a feature source for automatic meeting summarisation, which is useful for more efficiently satisfying people’s information needs about meeting contents.
Practical implications
The reader of an Information Systems (IS) journal would be interested in this paper because the work described and the findings gained could lead to the development of novel information systems that facilitate the work for businesses and individuals. Innovative meeting capture and retrieval applications, satisfying automatic summaries of important meeting points and sophisticated note-taking tools that suggest content automatically could make people’s daily lives more convenient in the future.
Social implications
There are wider implications in terms of productivity and efficiency. Business value is increased for the organisation, as human knowledge is built more or less automatically. There are also cognitive and social implications for individuals and possibly an impact on the society as a whole. It is also important for globalisation, social media and mobile devices.
Originality/value
The topic is new and original, as there has not been much research on it yet. Similar work was carried out recently (Murray, 2015; Bothin and Clough 2014). This is why it is relevant to an IS journal and interesting for the reader. In particular, dialogue acts about disfluencies and assessments appear to influence the note-taking activities. This behaviour is helpful as a feature source for automatic meeting summarisation, which is useful for more efficiently satisfying people’s information needs about meeting contents.
