Unsupervised text recap extraction for TV series

Abstract

Sequences found at the beginning of TV shows help the audience absorb the essence of previous episodes, and grab their attention with upcoming plots. In this paper, we propose a novel task, text recap extraction. Compared with conventional summarization, text recap extraction captures the duality of summarization and plot contingency between adjacent episodes. We present a new dataset, TVRecap, for text recap extraction on TV shows. We propose an unsupervised model that identifies text recaps based on plot descriptions. We introduce two contingency factors, concept coverage and sparse reconstruction, that encourage recaps to prompt the upcoming story development. We also propose a multi-view extension of our model which can incorporate dialogues and synopses. We conduct extensive experiments on TVRecap, and conclude that our model outperforms summarization approaches.