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In recent days, streaming technology has greatly promoted the development inthe field of livestream. Due to the excessive length of livestream records,it's quite essential to extract highlight segments with the aim of effectivereproduction and redistribution. Although there are lots of approaches provento be effective in the highlight detection for other modals, the challengesexisting in livestream processing, such as the extreme durations, large topicshifts, much irrelevant information and so forth, heavily hamper the adaptationand compatibility of these methods. In this paper, we formulate a new taskLivestream Highlight Detection, discuss and analyze the difficulties listedabove and propose a novel architecture AntPivot to solve this problem.Concretely, we first encode the original data into multiple views and modeltheir temporal relations to capture clues in a hierarchical attentionmechanism. Afterwards, we try to convert the detection of highlight clips intothe search for optimal decision sequences and use the fully integratedrepresentations to predict the final results in a dynamic-programmingmechanism. Furthermore, we construct a fully-annotated dataset AntHighlight toinstantiate this task and evaluate the performance of our model. The extensiveexperiments indicate the effectiveness and validity of our proposed method.

AntPivot: Livestream Highlight Detection via Hierarchical Attention Mechanism