# Product Title Refinement via Multi-Modal Generative Adversarial Learning

arXiv: https://arxiv.org/abs/1811.04498 · HTML: https://mlsanity.com/paper/1811.04498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

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

- [Unsupervised Meta Learning for One Shot Title Compression in Voice Commerce](https://mlsanity.com/paper/2102.10760/) (2021)

## Abstract (arXiv)

Nowadays, an increasing number of customers are in favor of using E-commerce Apps to browse and purchase products. Since merchants are usually inclined to employ redundant and over-informative product titles to attract customers' attention, it is of great importance to concisely display short product titles on limited screen of cell phones. Previous researchers mainly consider textual information of long product titles and lack of human-like view during training and evaluation procedure. In this paper, we propose a Multi-Modal Generative Adversarial Network (MM-GAN) for short product title generation, which innovatively incorporates image information, attribute tags from the product and the textual information from original long titles. MM-GAN treats short titles generation as a reinforcement learning process, where the generated titles are evaluated by the discriminator in a human-like view.