Generating Summaries of Sets of Consumer Products: Learning from Experiments

Kittipitch Kuptavanich, Ehud Reiter, Kees Van Deemter, Advaith Siddharthan


Abstract
We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset. When a set of consumer products is large and varied, it can be difficult for a consumer to understand how the products in the set differ; consequently, it can be challenging to choose the most suitable product from the set. To assist consumers, we generated high-level summaries of product sets. Two generation algorithms are presented, discussed, and evaluated with human users. Our evaluation results suggest a positive contribution to consumers’ understanding of the domain.
Anthology ID:
W18-6548
Volume:
Proceedings of the 11th International Conference on Natural Language Generation
Month:
November
Year:
2018
Address:
Tilburg University, The Netherlands
Editors:
Emiel Krahmer, Albert Gatt, Martijn Goudbeek
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
403–407
Language:
URL:
https://aclanthology.org/W18-6548
DOI:
10.18653/v1/W18-6548
Bibkey:
Cite (ACL):
Kittipitch Kuptavanich, Ehud Reiter, Kees Van Deemter, and Advaith Siddharthan. 2018. Generating Summaries of Sets of Consumer Products: Learning from Experiments. In Proceedings of the 11th International Conference on Natural Language Generation, pages 403–407, Tilburg University, The Netherlands. Association for Computational Linguistics.
Cite (Informal):
Generating Summaries of Sets of Consumer Products: Learning from Experiments (Kuptavanich et al., INLG 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-6548.pdf