The extractive summartization methods try to summarize a single or multiple documents based on informative sentences exactly as they appear in source(s). One method to choose these sentences is to use users’ query, which could be problematic in many cases, specially in scientific context. One way to tackle this challenge is to gather more information about the user and his preferences. Therefore, in this paper we propose a novel framework to use the users’ feedbacks and a social robotics platform, Nao robot, has been adapted as an interacting agent. This agent has multiple communication channels and could learn the user model and adapt to his/her needs via reinforcement learning approach. The whole approach is then studied in terms of how much it is able to adapt based on user’s feedback, and also in terms of interaction time.
Zarinbal Masouleh, Marzieh, Azadeh Mohebi, Hesamoddin Mosalli, Razieh Haratinik, and Zahra Jabalameli. 2019. A New Social Robot for Interactive Query-Based Summarization: Scientific Document Summarization. Paper Presented in The 4TH International Conference On Interactive Collaborative Robotics (ICR 2019), Istanbul.