Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2019
Abstract
In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.
Keywords
Natural Language Processing, Review Summarization, Text Generation, Neural Networks
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, November 3-7
First Page
2061
Last Page
2064
ISBN
9781450369763
Identifier
10.1145/3357384.3358142
Publisher
ACM
City or Country
New York
Citation
TIAN, Yufei; YU, Jianfei; and JIANG, Jing.
Aspect and opinion aware abstractive review summarization with reinforced hard typed decoder. (2019). CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, November 3-7. 2061-2064.
Available at: https://ink.library.smu.edu.sg/sis_research/4870
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3357384.3358142
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons