# Automatic Generation of Chinese Short Product Titles for Mobile Display

arXiv: https://arxiv.org/abs/1803.11359 · HTML: https://mlsanity.com/paper/1803.11359/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [AI-Generated Content (AIGC): A Survey](https://mlsanity.com/paper/2304.06632/) (2023)
- [Probing Product Description Generation via Posterior Distillation](https://mlsanity.com/paper/2103.01594/) (2021)
- [InstructPTS: Instruction-Tuning LLMs for Product Title Summarization](https://mlsanity.com/paper/2310.16361/) (2023)
- [EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data](https://mlsanity.com/paper/2312.15696/) (2023)
- [Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions](https://mlsanity.com/paper/2503.00501/) (2025)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [Unsupervised Meta Learning for One Shot Title Compression in Voice Commerce](https://mlsanity.com/paper/2102.10760/) (2021)

## Abstract (arXiv)

This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we propose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framework significantly outperforms several baselines by a substantial gain of 4.5%. Moreover, we produce an extractive summarization dataset for E-commerce short texts and will release it to the research community.