Automatically organizing papers in conference sessions using deep learning and network modeling

Thousands of papers are published at conferences every year. Conference organizers manually assign accepted papers to the sessions according to the title and keywords given by the author. Then organizer names each session, based on his/her experience. These are complex and time-consuming processes and often fail to collect papers on a similar topic in content. This often causes the participant to exit the session after listening to the presentation of one or two papers, because the session name does not fully represent the papers in the session and the papers in the session are not close in content. As a solution to these problems, this paper proposes a method for automatically organizing conference sessions. The method first introduces a network created with a deep learning-based document similarity. Then, sessions are determined with a community discovery method specific to this network, and finally, session titles are extracted with a topic modeling approach. To the best of our knowledge, this paper is the first effort in this direction. Experiments conducted on sessions of three real conferences show that the proposed method is able to create up to 21% better similar sessions, and session names better represent the papers in that session.