%0 Conference Proceedings %T ExtRA: Extracting Prominent Review Aspects from Customer Feedback %A Luo, Zhiyi %A Huang, Shanshan %A Xu, Frank F. %A Lin, Bill Yuchen %A Shi, Hanyuan %A Zhu, Kenny %Y Riloff, Ellen %Y Chiang, David %Y Hockenmaier, Julia %Y Tsujii, Jun’ichi %S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing %D 2018 %8 oct nov %I Association for Computational Linguistics %C Brussels, Belgium %F luo-etal-2018-extra %X Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost every day. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types. %R 10.18653/v1/D18-1384 %U https://aclanthology.org/D18-1384 %U https://doi.org/10.18653/v1/D18-1384 %P 3477-3486