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"Previously on ..." From Recaps to Story Summarization

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arxiv 2405.11487 v1 pith:2IRUV2W3 submitted 2024-05-19 cs.CV

classification cs.CV
keywords storysummarizationepisodesrecapsvideodialogepisodelong
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce multimodal story summarization by leveraging TV episode recaps - short video sequences interweaving key story moments from previous episodes to bring viewers up to speed. We propose PlotSnap, a dataset featuring two crime thriller TV shows with rich recaps and long episodes of 40 minutes. Story summarization labels are unlocked by matching recap shots to corresponding sub-stories in the episode. We propose a hierarchical model TaleSumm that processes entire episodes by creating compact shot and dialog representations, and predicts importance scores for each video shot and dialog utterance by enabling interactions between local story groups. Unlike traditional summarization, our method extracts multiple plot points from long videos. We present a thorough evaluation on story summarization, including promising cross-series generalization. TaleSumm also shows good results on classic video summarization benchmarks.

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  1. REGen: Multimodal Retrieval-Embedded Generation for Long-to-Short Video Editing

    cs.CV 2025-05 conditional novelty 5.0 of 10

    REGen generates documentary teasers by fine-tuning an LLM to write a script with <QUOTE> markers, then a trained retriever fills each marker with the most relevant clip from the source video.

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