Towards Opinion Summarization of Customer Reviews

Samuel Pecar


Abstract
In recent years, the number of texts has grown rapidly. For example, most review-based 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 introduce 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.
Anthology ID:
P18-3001
Volume:
Proceedings of ACL 2018, Student Research Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Vered Shwartz, Jeniya Tabassum, Rob Voigt, Wanxiang Che, Marie-Catherine de Marneffe, Malvina Nissim
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–8
Language:
URL:
https://aclanthology.org/P18-3001
DOI:
10.18653/v1/P18-3001
Bibkey:
Cite (ACL):
Samuel Pecar. 2018. Towards Opinion Summarization of Customer Reviews. In Proceedings of ACL 2018, Student Research Workshop, pages 1–8, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Towards Opinion Summarization of Customer Reviews (Pecar, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-3001.pdf