# Query-Focused Extractive Video Summarization

Source: [Query-Focused Extractive Video Summarization](<https://www.sota2.com/research/paper/68fa629c1e209131ce9fa2b1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:22:26.903Z

Authors: Aidean Sharghi, Boqing Gong, Mubarak Shah

Published: 2016-07-18T16:49:19.000Z

## Abstract

Video data is explosively growing. As a result of the "big video data", intelligent algorithms for automatic video summarization have re-emerged as a pressing need. We develop a probabilistic model, Sequential and Hierarchical Determinantal Point Process (SH-DPP), for query-focused extractive video summarization. Given a user query and a long video sequence, our algorithm returns a summary by selecting key shots from the video. The decision to include a shot in the summary depends on the shot's relevance to the user query and importance in the context of the video, jointly. We verify our approach on two densely annotated video datasets. The query-focused video summarization is particularly useful for search engines, e.g., to display snippets of videos.

## 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.

- [Query-focused Video Summarization on QFVS](<https://www.sota2.com/research/sota/query-focused-video-summarization-on-qfvs.md>): Query-focused Video Summarization; QFVS; Video Score 1: 35.67.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1607.05177>)

[PDF](<https://arxiv.org/pdf/1607.05177.pdf>)