Deep learning based conference program organization system from determining articles in session to scheduling

It is very important to create the conference programs correctly in terms of timing and content by preventing problems such as being of articles that do not have a common topic with each other in the same sessions, the parallel of the sessions containing articles on the same topic. It greatly affects the efficiency of conference for participants. Currently, conference programs are organized manually. Considering the conference scope and the number of articles in that conference, it is a difficult and time-consuming process. In this study, an automatic solution to this problem is presented. The use of the SBERT method is provided a more accurate calculation of article sim-ilarities compared to baseline methods and is increased the success of other stages. Unlike clas-sical clustering methods, an approach that clusters in such a way that there are equal numbers of data points in the clusters is proposed. In order to find the topic of the clusters determined as sessions, a topic determination approach is proposed that takes into account both keyword and article content similarities. Furthermore, with the proposed approach for session scheduling, the conference program has been planned more effectively by considering the parallel sessions. The ICTAI conference has been chosen to test the proposed approach. The proposed program is compared with both the real program and the programs created using Word2vec and Glove methods. With the proposed program, 10% improvement is achieved in terms of session simi-larity. In addition, parallel sessions are better planned with no conflicts compared to the real program.

Automated summary

This study proposes an automatic solution to create conference programs, which improves the efficiency of conference planning by using the SBERT method for article similarity calculation, proposing an equal clustering method, and considering both keyword and article content similarities for topic determination.