Augmentation of Human Memory: Anticipating Topics that Continue in the Next Meeting

Abstract

Memory augmentation is the process of providing human memory with information that facilitates and complements the recall of an event in a person»s past. Recently, there has been a lot of attention on processing the content of meetings for later reuse, such as reviewing a meeting for supporting failing memories, keeping in mind key issues, verification, etc. That is due to the fact that meetings are essential for sharing knowledge in organizations. In this paper, we propose four novel time-series methods for predicting the topics that one should review in preparation for a next meeting. The predicted/recommended topics can be reviewed by a user as a memory augmentation process to facilitate recall of key points of a previous meeting. With the growing number of meetings at an organization that one may attend weekly and with the growing number of topics discussed, forgetting past meetings becomes eminent, hence recommending certain topics to the user in order to prepare the user for a future meeting is beneficial and important. Our experimental results on real-world data, demonstrate that our methods significantly outperform a state-of-the-art Hidden Markov Model baseline. This indicates the efficacy of our proposed methods for modeling semantics in temporal data.