Unsupervised Text Recap Extraction for TV Series
Unsupervised Text Recap Extraction for TV Series Hongliang Yu and Shikun Zhang and Louis-Philippe Morency Language Technologies Institute Carnegie Mellon University Pittsburgh, PA, 15213, USA yuhongliang, shikunz, morency @cs.cmu.edu { } Abstract absorb the essence of previous episodes, but also grab people’s attention with upcoming plots. How- Sequences found at the beginning of TV ever, creating those recaps for every newly aired shows help the audience absorb the essence episode is labor-intensive and time-consuming. To of previous episodes, and grab their attention with upcoming plots. In this paper, we pro- our advantage, there are many textual scripts freely pose a novel task, text recap extraction. Com- available online which describe the events and ac- 2 pared with conventional summarization, text tions happening during the TV show episodes . recap extraction captures the duality of sum- These textual scripts contain plot descriptions of the marization and plot contingency between ad- events, dialogues of the actors, and sometimes also jacent episodes. We present a new dataset, the synopsis summarizing the whole episode. TVRecap, for text recap extraction on TV These abundant textual resources enable us to shows. We propose an unsupervised model that identifies text recaps based on plot de- study a novel, yet challenging task: automatic scriptions. We introduce two contingency fac- text recap extraction, illustrated in Figure 1. The tors, concept coverage and sparse reconstruc- goal of text recap extraction is to identify seg- tion, that encourage recaps to prompt the up- ments from scripts which both summarize the cur- coming story development. We also propose a rent episode and prompt the story development of multi-view extension of our model which can the next episode.
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