This article presents a new dataset of 100 movie scripts with human-annotated key scenes and proposes a two-stage model, SELECT & SUMM, which first identifies key scenes and then generates summaries using only those scenes. The approach outperforms previous models in accuracy and efficiency, making movie script summarization more scalable and informative.
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