Understand
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.
Built on
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Earlier work this paper cites.
Data generation as sequential decision making
Philip Bachman and Doina Precup · 2015
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
Earlier work this paper cites.
Similar
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Cited alongside, same era.
Attsum: Joint learning of focusing and summarization with neural attention
Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei, and Yanran Li · 2016
Cited alongside, same era.
Distraction-based neural networks for document summarization
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, and Hui Jiang · 2016
Cited alongside, same era.
Abstractive sentence summarization with attentive recurrent neural networks
Sumit Chopra, Michael Auli, and Alexander M Rush · 2016
Cited alongside, same era.
Language as a latent variable: Discrete generative models for sentence compression
Yishu Miao and Phil Blunsom · 2016
Cited alongside, same era.
Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2016
Cited alongside, same era.
Then
Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky · 2017
Later among the works it cites.
Summarunner: A recurrent neural network based sequence model for extractive summarization of documents
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou · 2017
Later among the works it cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning · 2017
Later among the works it cites.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Later among the works it cites.
Automatic generation of chinese short product titles for mobile display
Yu Gong, Xusheng Luo, Kenny Q Zhu, Shichen Liu, and Wenwu Ou · 2018
Closest in time.
A multi-task learning approach for improving product title compression with user search log data
Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si, and Man Lan · 2018
Closest in time.
Beyond the bibliography
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…