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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.
Non-autoregressive Korean Morphological Analysis with Word Segment Information
http://doi.org/10.5626/JOK.2023.50.8.653
This paper introduces a non-autoregressive Korean morphological analyzer. The proposed morphological analyzer utilizes a transformer encoder to encode a given sentence and employs two non-autoregressive decoders for morphological analysis. Each decoder generates a morpheme sequence and a corresponding POS tag sequence, which are then combined to produce the final morphological analysis. Additionally, this paper leverages word segment information within the sentence to predict the target sequence length, mitigating performance degradation resulting from incorrect target sequence length predictions. Experimental results show that the proposed non-autoregressive Korean morphological analyzer outperforms all non-autoregressive baselines. It achieves comparable accuracy to an autoregressive Korean morphological analyzer while it performs nearly 14.76 times faster than the autoregressive Korean morphological analyzer.
Solving Factual Inconsistency in Abstractive Summarization using Named Entity Fact Discrimination
Jeongwan Shin, Yunseok Noh, Hyun-Je Song, Seyoung Park
http://doi.org/10.5626/JOK.2022.49.3.231
Factual inconsistency in abstractive summarization is a problem that a generated summary can be factually inconsistent with a source text. Previous studies adopted a span selection that replaced entities in the generated summary with entities in the source text because most inconsistencies are related to incorrect entities. These studies assumed that all entities in the generated summary were inconsistent and tried to replace all entities with other entities. However, this was problematic because some consistent entities could be replaced and masked, so information on consistent entities was lost. This paper proposes a method that sequentially executes a fact discriminator and a fact corrector to solve this problem. The fact discriminator determines the inconsistent entities, and the fact corrector replaces only the inconsistent entities. Since the fact corrector corrects only the inconsistent entities, it utilizes the consistent entities. Experiments show that the proposed method boosts the factual consistency of system-generated summaries and outperforms the baselines in terms of both automatic metrics and human evaluation.
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