Time series classification (TSC) presents significant challenges in data analytics and plays an essential role in industries such as manufacturing, healthcare, and finance. Existing research has utilized sequence models, such as long short-term memory (LSTM) and transformers, to achieve high performance; however, these models fail to fully address the inherent challenges of capturing long-term dependencies in time series data. Recently, efforts have been made to overcome these limitations by processing time series data through conversion into images. One such method, the Gramian Angular Field (GAF), converts time series data into images that visually represent the relationship between time points, effectively capturing global trends that traditional sequence models often miss. However, single-modality approaches, which rely on only one representation of time series data, still face limitations in comprehensively capturing the diverse features of a time series. Additionally, existing multi-modality approaches may overlook the detailed features between time points. To address these issues, this article proposes a patch-level hybrid contrastive learning model that combines transformer with Vision Transformer (ViT). The proposed model converts a one-dimensional time series into GAF images and leverages contrastive learning to robustly learn fine-grained features. This enables the model to effectively capture both temporal relationships between time points and spatial relationships between patches, thereby enhancing its generalization capabilities. Experimental results demonstrate that the proposed model outperforms the existing methods on the UCR TSC dataset. This shows that the model can overcome the limitations of single-modality approaches by integrating the complex structural features of time series data through a multi-modality framework, ultimately leading to improved classification performance. This approach provides a new avenue for enhancing TSC and holds promise for applications across various industries.

Most studies on abstractive summarization are conducted in a supervised learning framework, aiming to generate a golden summary from the original document. In this process, the model focuses on portions of the document that closely resemble the golden summary to produce a coherent output. Consequently, current methodologies tend to achieve higher performance on extractive datasets compared to abstractive datasets, indicating diminished effectiveness on more abstracted content. To address this, our study proposes a methodology that maintains high effectiveness on abstractive datasets. Specifically, we introduce a multi-task learning approach that incorporates both salient and non-salient information during training. This is implemented by adding a contrastive objective to the fine-tuning phase of an encoder–decoder language model. Salient and non-salient parts are selected based on ROUGE-L F1 scores, and their relationships are learned through a triplet loss function. The proposed method is evaluated on five benchmark summarization datasets, including two extractive and three abstractive datasets. Experimental results demonstrate significant performance improvements on abstractive datasets, particularly those with high levels of abstraction, compared to existing abstractive summarization methods.

Recent methodologies have achieved good performance in objectively summarizing important information from fact-based datasets such as Extreme Summarization and CNN Daily Mail. These methodologies involve abstractive summarization, extracting the core content from an input text and transforming it into natural sentences. Unlike fact-based documents, opinion-based documents require a thorough analysis of sentiment and understanding of the writer’s intention. However, existing models do not explicitly consider these factors. Therefore, in this study, we propose a novel text summarization model that is specifically designed for opinion-based documents. Specifically, we identify the sentiment distribution of the entire document and train the summarization model to focus on major opinions that conform to the intended message while randomly masking minor opinions. Experimental results show that the proposed model outperforms existing summarization models in summarizing opinion-based documents, effectively capturing and highlighting the main opinions in the generated abstractive summaries.

Text augmentation is a strategy for increasing the diversity of training examples without explicitly collecting new data. Owing to the efficiency and effectiveness of text augmentation, numerous augmentation methodologies have been proposed. Among them, the method based on modification, particularly the mix-up method of swapping words between two or more sentences, is widely used because it can be applied simply and shows good levels of performance. However, the existing mix-up approaches are limited; they do not reflect the importance of the manipulated word. That is, even if a word that has a critical effect on the classification result is manipulated, it is not considered significant in labeling the augmented data. Therefore, in this study, we propose an effective text augmentation technique that explicitly derives the importance of manipulated words and reflects this importance in the labeling of augmented data. The importance of each word, in other words, explainability, is calculated, and this is explicitly reflected in the labeling process of the augmented data. The results of the experiment confirmed that when the importance of the manipulated word was reflected in the labeling, the performance was significantly higher than that of the existing methods.

Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.