# TCR: Short Video Title Generation and Cover Selection with Attention Refinement

arXiv: https://arxiv.org/abs/2304.12561 · HTML: https://mlsanity.com/paper/2304.12561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 17 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [UniT: Multimodal Multitask Learning with a Unified Transformer](https://mlsanity.com/paper/2102.10772/) (2021)
- [How2: A Large-scale Dataset for Multimodal Language Understanding](https://mlsanity.com/paper/1811.00347/) (2018)
- [Multi-modal Dense Video Captioning](https://mlsanity.com/paper/2003.07758/) (2020)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://mlsanity.com/paper/2004.12393/) (2020)
- [VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles](https://mlsanity.com/paper/2010.05406/) (2020)
- [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://mlsanity.com/paper/1609.01388/) (2016)
- [Learning to Discretely Compose Reasoning Module Networks for Video Captioning](https://mlsanity.com/paper/2007.09049/) (2020)
- [MAST: Multimodal Abstractive Summarization with Trimodal Hierarchical Attention](https://mlsanity.com/paper/2010.08021/) (2020)
- [NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems](https://mlsanity.com/paper/1706.00457/) (2017)

## Built on by (most cited first, 1 of 1)

- [From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions](https://mlsanity.com/paper/2402.17633/) (2024)

## Abstract (arXiv)

With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab viewers' attention. Existing studies on video captioning mostly focus on generating factual descriptions of actions, which do not conform to video titles intended for catching viewer attention. Furthermore, research for cover selection based on multimodal information is sparse. These problems motivate the need for tailored methods to specifically support the joint task of short video title generation and cover selection (TG-CS) as well as the demand for creating corresponding datasets to support the studies. In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers. We then propose a Title generation and Cover selection with attention Refinement (TCR) method for TG-CS. The refinement procedure progressively selects high-quality samples and highly relevant frames and text tokens within each sample to refine model training. Extensive experiments show that our TCR method is superior to various existing video captioning methods in generating titles and is able to select better covers for noisy real-world short videos.