Generating E-Commerce Product Titles and Predicting their Quality

José G. Camargo de Souza, Michael Kozielski, Prashant Mathur, Ernie Chang, Marco Guerini, Matteo Negri, Marco Turchi, Evgeny Matusov


Abstract
E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios.
Anthology ID:
W18-6530
Volume:
Proceedings of the 11th International Conference on Natural Language Generation
Month:
November
Year:
2018
Address:
Tilburg University, The Netherlands
Venues:
INLG | WS
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
233–243
Language:
URL:
https://www.aclweb.org/anthology/W18-6530
DOI:
10.18653/v1/W18-6530
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/W18-6530.pdf