# Beyond the Frame: Single and mutilple video summarization method with user-defined length

arXiv: https://arxiv.org/abs/2401.10254 · HTML: https://mlsanity.com/paper/2401.10254/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 60 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Video Summarization with Attention-Based Encoder-Decoder Networks](https://mlsanity.com/paper/1708.09545/) (2017)
- [Pretraining-Based Natural Language Generation for Text Summarization](https://mlsanity.com/paper/1902.09243/) (2019)
- [Automatic Text Summarization Methods: A Comprehensive Review](https://mlsanity.com/paper/2204.01849/) (2022)
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- [Chatbot for fitness management using IBM Watson](https://mlsanity.com/paper/2112.15167/) (2021)
- [Machine Learning and Artificial Intelligence in Next-Generation Wireless Network](https://mlsanity.com/paper/2202.01690/) (2021)
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- [Heterogeneous Computing Systems](https://mlsanity.com/paper/2212.14418/) (2022)
- [Security, Privacy and Challenges in Microservices Architecture and Cloud Computing- Survey](https://mlsanity.com/paper/2212.14422/) (2022)

## Abstract (arXiv)

Video smmarization is a crucial method to reduce the time of videos which reduces the spent time to watch/review a long video. This apporach has became more important as the amount of publisehed video is increasing everyday. A single or multiple videos can be summarized into a relatively short video using various of techniques from multimodal audio-visual techniques, to natural language processing approaches. Audiovisual techniques may be used to recognize significant visual events and pick the most important parts, while NLP techniques can be used to evaluate the audio transcript and extract the main sentences (timestamps) and corresponding video frames from the original video. Another approach is to use the best of both domain. Meaning that we can use audio-visual cues as well as video transcript to extract and summarize the video. In this paper, we combine a variety of NLP techniques (extractive and contect-based summarizers) with video processing techniques to convert a long video into a single relatively short video. We design this toll in a way that user can specify the relative length of the summarized video. We have also explored ways of summarizing and concatenating multiple videos into a single short video which will help having most important concepts from the same subject in a single short video. Out approach shows that video summarizing is a difficult but significant work, with substantial potential for further research and development, and it is possible thanks to the development of NLP models.