# Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos Source: [Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:58:16.069Z Authors: Mingfei Han, Linjie Yang, Xiaojun Chang, Lina Yao, Heng Wang Published: 2023-12-16T03:17:30.000Z ## Abstract A short clip of video may contain progression of multiple events and an interesting story line. A human need to capture both the event in every shot and associate them together to understand the story behind it. In this work, we present a new multi-shot video understanding benchmark Shot2Story with detailed shot-level captions, comprehensive video summaries and question-answering pairs. To facilitate better semantic understanding of videos, we provide captions for both visual signals and human narrations. We design several distinct tasks including single-shot video captioning, multi-shot video summarization, and multi-shot video question answering. Preliminary experiments show some challenges to generate a long and comprehensive video summary for multi-shot videos. Nevertheless, the generated imperfect summaries can already achieve competitive performance on existing video understanding tasks such as video question-answering, promoting an under-explored setting of video understanding with detailed summaries. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()