A Statistical Framework for Product Description Generation

Jinpeng Wang, Yutai Hou, Jing Liu, Yunbo Cao, Chin-Yew Lin


Abstract
We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.
Anthology ID:
I17-2032
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
187–192
Language:
URL:
https://www.aclweb.org/anthology/I17-2032
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-2032.pdf