# Video Summarization with Long Short-term Memory

arXiv: https://arxiv.org/abs/1605.08110 · HTML: https://mlsanity.com/paper/1605.08110/

## Key facts

- Cited by 142 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2016.

## Built on by (most cited first, 30 of 142)

- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [UniVTG: Towards Unified Video-Language Temporal Grounding](https://mlsanity.com/paper/2307.16715/) (2023)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [EgoVLPv2: Egocentric Video-Language Pre-training with Fusion in the Backbone](https://mlsanity.com/paper/2307.05463/) (2023)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Temporal Action Segmentation: An Analysis of Modern Techniques](https://mlsanity.com/paper/2210.10352/) (2022)
- [An Outlook into the Future of Egocentric Vision](https://mlsanity.com/paper/2308.07123/) (2023)
- [Align and Attend: Multimodal Summarization with Dual Contrastive Losses](https://mlsanity.com/paper/2303.07284/) (2023)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning](https://mlsanity.com/paper/2404.12353/) (2024)
- [Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey](https://mlsanity.com/paper/2108.11510/) (2021)
- [VideoXum: Cross-modal Visual and Textural Summarization of Videos](https://mlsanity.com/paper/2303.12060/) (2023)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [Cross-category Video Highlight Detection via Set-based Learning](https://mlsanity.com/paper/2108.11770/) (2021)
- [FOCUS: Efficient Keyframe Selection for Long Video Understanding](https://mlsanity.com/paper/2510.27280/) (2025)
- [TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliency](https://mlsanity.com/paper/2208.06773/) (2022)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [R2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding](https://mlsanity.com/paper/2404.00801/) (2024)
- [Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding](https://mlsanity.com/paper/2311.08835/) (2023)
- [BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2312.00083/) (2023)
- [Toward Unifying Text Segmentation and Long Document Summarization](https://mlsanity.com/paper/2210.16422/) (2022)
- [End-to-end Dense Video Captioning as Sequence Generation](https://mlsanity.com/paper/2204.08121/) (2022)
- [Supervised Video Summarization via Multiple Feature Sets with Parallel Attention](https://mlsanity.com/paper/2104.11530/) (2021)
- [GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization](https://mlsanity.com/paper/2104.12465/) (2021)
- [LvBench: A Benchmark for Long-form Video Understanding with Versatile Multi-modal Question Answering](https://mlsanity.com/paper/2312.04817/) (2023)
- [Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2404.09263/) (2024)
- [CSTA: CNN-based Spatiotemporal Attention for Video Summarization](https://mlsanity.com/paper/2405.11905/) (2024)

## Abstract (arXiv)

We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM), a special type of recurrent neural networks to model the variable-range dependencies entailed in the task of video summarization. Our learning models attain the state-of-the-art results on two benchmark video datasets. Detailed analysis justifies the design of the models. In particular, we show that it is crucial to take into consideration the sequential structures in videos and model them. Besides advances in modeling techniques, we introduce techniques to address the need of a large number of annotated data for training complex learning models. There, our main idea is to exploit the existence of auxiliary annotated video datasets, albeit heterogeneous in visual styles and contents. Specifically, we show domain adaptation techniques can improve summarization by reducing the discrepancies in statistical properties across those datasets.