TY - GEN
T1 - Generating product descriptions from user reviews
AU - Novgorodov, Slava
AU - Guy, Ido
AU - Elad, Guy
AU - Radinsky, Kira
N1 - Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.
PY - 2019/5/13
Y1 - 2019/5/13
N2 - Product descriptions play an important role in the e-commerce ecosystem, conveying to buyers information about a merchandise they may purchase. Yet, on leading e-commerce websites, with high volumes of new items offered for sale every day, product descriptions are often lacking or missing altogether. Moreover, many descriptions include information that holds little value and sometimes even disrupts buyers, in an attempt to draw attention and purchases. In this work, we suggest to mitigate these issues by generating short crowd-based product descriptions from user reviews. We apply an extractive approach, where review sentences are used in their original form to compose the product description. At the core of our method is a supervised approach to identify candidate review sentences suitable to be used as part of a description. Our analysis, based on data from both the Fashion and Motors domains, reveals the top reasons for review sentences being unsuitable for the product's description and these are used, in turn, as part of a deep multi-task learning architecture. We then diversify the set of candidates by removing redundancies and, at the final step, select the top candidates to be included in the description. We compare different methods for each step and also conduct an end-to-end evaluation, based on rating from professional annotators, showing the generated descriptions are of high quality.
AB - Product descriptions play an important role in the e-commerce ecosystem, conveying to buyers information about a merchandise they may purchase. Yet, on leading e-commerce websites, with high volumes of new items offered for sale every day, product descriptions are often lacking or missing altogether. Moreover, many descriptions include information that holds little value and sometimes even disrupts buyers, in an attempt to draw attention and purchases. In this work, we suggest to mitigate these issues by generating short crowd-based product descriptions from user reviews. We apply an extractive approach, where review sentences are used in their original form to compose the product description. At the core of our method is a supervised approach to identify candidate review sentences suitable to be used as part of a description. Our analysis, based on data from both the Fashion and Motors domains, reveals the top reasons for review sentences being unsuitable for the product's description and these are used, in turn, as part of a deep multi-task learning architecture. We then diversify the set of candidates by removing redundancies and, at the final step, select the top candidates to be included in the description. We compare different methods for each step and also conduct an end-to-end evaluation, based on rating from professional annotators, showing the generated descriptions are of high quality.
KW - Deep multi-task leaning
KW - Electronic commerce
KW - Language generation
KW - User-generated content
UR - http://www.scopus.com/inward/record.url?scp=85066904854&partnerID=8YFLogxK
U2 - 10.1145/3308558.3313532
DO - 10.1145/3308558.3313532
M3 - Conference contribution
AN - SCOPUS:85066904854
T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
SP - 1354
EP - 1364
BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
PB - Association for Computing Machinery, Inc
T2 - 2019 World Wide Web Conference, WWW 2019
Y2 - 13 May 2019 through 17 May 2019
ER -