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Aspect-Based Comparative Summarization with Large Language Model
http://doi.org/10.5626/JOK.2025.52.7.579
This paper proposes an aspect-based comparative summarization method to generate summary comparisons between two items based on their reviews, aiming to assist users in making informed decisions. Given the reviews of two items, aspects are dynamically generated from each review using a large language model. To identify common aspects for comparison, the generated aspect lists of both items are merged. The review sentences of each item are classified into the most relevant aspects, and then the summarization process removes redundant and unnecessary information. Subsequently, an abstract summary is generated for each common aspect to capture the overall content of the reviews. Experiments were conducted in the domains of hotels, electronic devices, and furniture, comparing human-written summaries with system-generated ones. The proposed method demonstrated superior summarization performance compared to existing comparison models.
Prompt Tuning For Korean Aspect-Based Sentiment Analysis
Bong-Su Kim, Seung-Ho Choi, Si-hyun Park, Jun-Ho Wang, Ji-Yoon Kim, Hyun-Kyu Jeon, Jung-Hoon Jang
http://doi.org/10.5626/JOK.2024.51.12.1043
Aspect-based sentiment analysis examines how emotions in text relate to specific aspects, such as product characteristics or service features. This paper presents a comprehensive methodology for applying prompt tuning techniques to multi-task token labeling challenges using aspect-based sentiment analysis data. The methodology includes a pipeline for identifying emotion expression domains, which generalizes the token labeling problem into a sequence labeling problem. It also suggests selecting templates to classify separated sequences based on aspects and emotions, and expanding label words to align with the dataset’s characteristics, thus optimizing the model's performance. Finally, the paper provides several experimental results and analyses for the aspect-based sentiment analysis task in a few-shot setting. The constructed data and baseline model are available on AIHUB. (www.aihub.or.kr).
Aspect Summarization for Product Reviews based on Attention-based Aspect Extraction
Jun-Nyeong Jeong, Sang-Young Kim, Seong-Tae Kim, Jeong-Jae Lee, Yuchul Jung
http://doi.org/10.5626/JOK.2021.48.12.1318
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.
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