  Deriving event storylines is an effective summarization method to succinctly
organize extensive information, which can significantly alleviate the pain of
information overload. The critical challenge is the lack of widely recognized
definition of storyline metric. Prior studies have developed various approaches
based on different assumptions about users' interests. These works can extract
interesting patterns, but their assumptions do not guarantee that the derived
patterns will match users' preference. On the other hand, their exclusiveness
of single modality source misses cross-modality information. This paper
proposes a method, multimodal imitation learning via generative adversarial
networks(MIL-GAN), to directly model users' interests as reflected by various
data. In particular, the proposed model addresses the critical challenge by
imitating users' demonstrated storylines. Our proposed model is designed to
learn the reward patterns given user-provided storylines and then applies the
learned policy to unseen data. The proposed approach is demonstrated to be
capable of acquiring the user's implicit intent and outperforming competing
methods by a substantial margin with a user study.
