TY  - JOUR
T1  - Aspect Summarization for Product Reviews based on Attention-based Aspect Extraction
AU  - Jeong, Jun-Nyeong 
AU  - Kim, Sang-Young 
AU  - Kim, Seong-Tae 
AU  - Lee, Jeong-Jae 
AU  - Jung, Yuchul 
JO  - Journal of KIISE, JOK
PY  - 2021
DA  - 2021/1/14
DO  - 10.5626/JOK.2021.48.12.1318
KW  - aspect extraction
KW  - text summarization
KW  - aspect
KW  - review
KW  - transformer
AB  - Recently, document summaries such as articles and papers through machine learning and summary-related research on online reviews are active. In this study, unlike the existing simply summarizing content, a technique was developed for generating an aspect summary by considering various aspects of product reviews. By refining the earphone product review data crawled to build the learning data, 40,000 reviews were obtained. Moreover, we manually constructed 4,000 aspect summaries to be used for our training and evaluation tasks. In particular, we proposed a model that could summarize aspects using only text data using the aspect-based word expansion technique (ABAE). To judge the effectiveness of the proposed technique, we performed experiments according to the use of words related to aspects and the masking ratio during learning. As a result, it was confirmed that the model that randomly masked 25% of the words related to the aspect showed the highest performance, and during verification, the ROUGE was 0.696, and the BERTScore was 0.879.