Towards Opinion Summarization of Customer Reviews

Samuel Pecar
2018 Proceedings of ACL 2018, Student Research Workshop  
In recent years, the number of texts has grown rapidly. For example, most reviewbased portals, like Yelp or Amazon, contain thousands of user-generated reviews. It is impossible for any human reader to process even the most relevant of these documents. The most promising tool to solve this task is a text summarization. Most existing approaches, however, work on small, homogeneous, English datasets, and do not account to multi-linguality, opinion shift, and domain effects. In this paper, we
more » ... duce our research plan to use neural networks on user-generated travel reviews to generate summaries that take into account shifting opinions over time. We outline future directions in summarization to address all of these issues. By resolving the existing problems, we will make it easier for users of review-sites to make more informed decisions.
doi:10.18653/v1/p18-3001 dblp:conf/acl/Pecar18 fatcat:5d3p4w6gvvfixdvp2jpcq6l4ia