@inproceedings{camargo-de-souza-etal-2018-generating,
title = "Generating {E}-Commerce Product Titles and Predicting their Quality",
author = "Camargo de Souza, Jos{\'e} G. and
Kozielski, Michael and
Mathur, Prashant and
Chang, Ernie and
Guerini, Marco and
Negri, Matteo and
Turchi, Marco and
Matusov, Evgeny",
editor = "Krahmer, Emiel and
Gatt, Albert and
Goudbeek, Martijn",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
month = nov,
year = "2018",
address = "Tilburg University, The Netherlands",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6530",
doi = "10.18653/v1/W18-6530",
pages = "233--243",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Generating E-Commerce Product Titles and Predicting their Quality
%A Camargo de Souza, José G.
%A Kozielski, Michael
%A Mathur, Prashant
%A Chang, Ernie
%A Guerini, Marco
%A Negri, Matteo
%A Turchi, Marco
%A Matusov, Evgeny
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F camargo-de-souza-etal-2018-generating
%X 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.
%R 10.18653/v1/W18-6530
%U https://aclanthology.org/W18-6530
%U https://doi.org/10.18653/v1/W18-6530
%P 233-243
Markdown (Informal)
[Generating E-Commerce Product Titles and Predicting their Quality](https://aclanthology.org/W18-6530) (Camargo de Souza et al., INLG 2018)
ACL
- José G. Camargo de Souza, Michael Kozielski, Prashant Mathur, Ernie Chang, Marco Guerini, Matteo Negri, Marco Turchi, and Evgeny Matusov. 2018. Generating E-Commerce Product Titles and Predicting their Quality. In Proceedings of the 11th International Conference on Natural Language Generation, pages 233–243, Tilburg University, The Netherlands. Association for Computational Linguistics.