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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.
Language Style Transfer Based on Surface-Level Neutralization
Wooyong Choi, Yunseok Noh, Seyoung Park
http://doi.org/10.5626/JOK.2020.47.2.162
Two main concerns of language style transfer such as sentiment transfer are neutralization of a stylized sentence and re-stylization of the neutralized sentence with a target style. Generally, neutralization is accomplished by learning a neutralized latent space by adversarial learning. However, this neutralization method suffers from the difficulty of maintaining the original content after style transfer. In this paper, we propose a two-step language style transfer method comprised of a surface-level neutralization that removes style words and a target-style word prediction for the removed words. For this, a self-attentive style classifier and style-specific word predictors are used for the surface neutralization and style word generation, respectively. To evaluate the proposed method, several experiments of language style transfer were conducted with Yelp and Amazon review datasets and Caption dataset. As a result, the proposed method shows superior performance over baseline methods on various evaluation metrics including automatic and human evaluations.
Solving for Redundant Repetition Problem of Generating Summarization using Decoding History
Jaehyun Ryu, Yunseok Noh, Su Jeong Choi, Seyoung Park, Seong-Bae Park
http://doi.org/10.5626/JOK.2019.46.6.535
Neural attentional sequence-to-sequence models have achieved great success in abstractive summarization. However, the model is limited by several challenges including repetitive generation of words, phrase and sentences in the decoding step. Many studies have attempted to address the problem by modifying the model structure. Although the consideration of actual history of word generation is crucial to reduce word repetition, these methods, however, do not consider the decoding history of generated sequence. In this paper, we propose a new loss function, called ‘Repeat Loss’ to avoid repetitions. The Repeat Loss directly prevents the model from repetitive generation of words by giving a loss penalty to the generation probability of words already generated in the decoding history. Since the propose Repeat Loss does not need a special network structure, the loss function is applicable to any existing sequence-to-sequence models. In experiments, we applied the Repeat Loss to a number of sequence-to-sequence model based summarization systems and trained them on both Korean and CNN/Daily Mail summarization datasets. The results demonstrate that the proposed method reduced repetitions and produced high-quality summarization.
Regularizing Korean Conversational Model by Applying Denoising Mechanism
Tae-Hyeong Kim, Yunseok Noh, Seong-Bae Park, Se-Yeong Park
http://doi.org/10.5626/JOK.2018.45.6.572
A conversation system is a system that responds appropriately to input utterances. Recently, the sequence-to-sequence framework has been widely used as a conversation-learning model. However, the conversation model learned in such a way often generates a safe and dull response that does not provide appropriate information or sophisticated meaning. In addition, this model is also useless for input utterances appearing in various forms, such as with changed ending words or changed word order. To solve these problems, we propose a denoising response generation model applying a denoising mechanism. By injecting noise into original input, the proposed method creates a model that will stochastically experience new input made up of items that were not included in the original data during the training process. This data augmentation effect regularizes the model and allows the realization of a robust model. We evaluate our model using 90k input utterances-responses from Korean conversation pair data. The proposed model achieves better results compared to a baseline model on both ROUGE F1 score and qualitative evaluations by human annotators.
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