# Comprehensive Information Integration Modeling Framework for Video Titling

arXiv: https://arxiv.org/abs/2006.13608 · HTML: https://mlsanity.com/paper/2006.13608/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 16 of 16)

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- [Analysis and Applications of Class-wise Robustness in Adversarial Training](https://mlsanity.com/paper/2105.14240/) (2021)
- [End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding](https://mlsanity.com/paper/2203.08013/) (2022)
- [Adaptive Hierarchical Graph Reasoning with Semantic Coherence for Video-and-Language Inference](https://mlsanity.com/paper/2107.12270/) (2021)
- [Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training](https://mlsanity.com/paper/2108.11119/) (2021)
- [Why Do We Click: Visual Impression-aware News Recommendation](https://mlsanity.com/paper/2109.12651/) (2021)
- [Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos](https://mlsanity.com/paper/2208.01954/) (2022)
- [Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs](https://mlsanity.com/paper/2504.00072/) (2025)
- [CREATE: A Benchmark for Chinese Short Video Retrieval and Title Generation](https://mlsanity.com/paper/2203.16763/) (2022)
- [RETTA: Retrieval-Enhanced Test-Time Adaptation for Zero-Shot Video Captioning](https://mlsanity.com/paper/2405.07046/) (2024)
- [Knowledge Guided Entity-aware Video Captioning and A Basketball Benchmark](https://mlsanity.com/paper/2401.13888/) (2024)
- [Modeling High-order Interactions across Multi-interests for Micro-video Recommendation](https://mlsanity.com/paper/2104.00305/) (2021)

## Abstract (arXiv)

In e-commerce, consumer-generated videos, which in general deliver consumers' individual preferences for the different aspects of certain products, are massive in volume. To recommend these videos to potential consumers more effectively, diverse and catchy video titles are critical. However, consumer-generated videos seldom accompany appropriate titles. To bridge this gap, we integrate comprehensive sources of information, including the content of consumer-generated videos, the narrative comment sentences supplied by consumers, and the product attributes, in an end-to-end modeling framework. Although automatic video titling is very useful and demanding, it is much less addressed than video captioning. The latter focuses on generating sentences that describe videos as a whole while our task requires the product-aware multi-grained video analysis. To tackle this issue, the proposed method consists of two processes, i.e., granular-level interaction modeling and abstraction-level story-line summarization. Specifically, the granular-level interaction modeling first utilizes temporal-spatial landmark cues, descriptive words, and abstractive attributes to builds three individual graphs and recognizes the intra-actions in each graph through Graph Neural Networks (GNN). Then the global-local aggregation module is proposed to model inter-actions across graphs and aggregate heterogeneous graphs into a holistic graph representation. The abstraction-level story-line summarization further considers both frame-level video features and the holistic graph to utilize the interactions between products and backgrounds, and generate the story-line topic of the video. We collect a large-scale dataset accordingly from real-world data in Taobao, a world-leading e-commerce platform, and will make the desensitized version publicly available to nourish further development of the research community...