• Smart Media Journal
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• v.10 no.1
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• pp.39-46
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• 2021

This study is about a method of extracting a summary from a news article in consideration of the importance of each sentence constituting the article. We propose a method of calculating sentence importance by extracting the probabilities of topic sentence, similarity with article title and other sentences, and sentence position as characteristics that affect sentence importance. At this time, a hypothesis is established that the Topic Sentence will have a characteristic distinct from the general sentence, and a deep learning-based classification model is trained to obtain a topic sentence probability value for the input sentence. Also, using the pre-learned ELMo language model, the similarity between sentences is calculated based on the sentence vector value reflecting the context information and extracted as sentence characteristics. The topic sentence classification performance of the LSTM and BERT models was 93% accurate, 96.22% recall, and 89.5% precision, resulting in high analysis results. As a result of calculating the importance of each sentence by combining the extracted sentence characteristics, it was confirmed that the performance of extracting the topic sentence was improved by about 10% compared to the existing TextRank algorithm.

• The Journal of Information Systems
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• v.31 no.1
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• pp.175-195
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• 2022

Purpose The purpose of this study is to examine news articles and analyze trends and key agendas related to artificial intelligence(AI). In particular, this study tried to compare the reporting behaviors of Korea and the United States, which is considered to be a leader in the field of AI. Design/methodology/approach This study analyzed news articles using a big data method. Specifically, main agendas of the two countries were derived and compared through the keyword frequency analysis, topic modeling, and language network analysis. Findings As a result of the keyword analysis, the introduction of AI and related services were reported importantly in Korea. In the US, the war of hegemony led by giant IT companies were widely covered in the media. The main topics in Korean media were 'Strategy in the 4th Industrial Revolution Era', 'Building a Digital Platform', 'Cultivating Future human resources', 'Building AI applications', 'Introduction of Chatbot Services', 'Launching AI Speaker', and 'Alphago Match'. The main topics of US media coverage were 'The Bright and Dark Sides of Future Technology', 'The War of Technology Hegemony', 'The Future of Mobility', 'AI and Daily Life', 'Social Media and Fake News', and 'The Emergence of Robots and the Future of Jobs'. The keywords with high centrality in Korea were 'release', 'service', 'base', 'robot', 'era', and 'Baduk or Go'. In the US, they were 'Google', 'Amazon', 'Facebook', 'China', 'Car', and 'Robot'.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.548-562
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• 2022

This study was conducted to diagnose the issues of AI ethics in global content and to discuss what kind of discourse is needed to strengthen human identity. To this end, the study selected Netflix original content "The Social Dilemma" for analysis and adopted narrative analysis as the research method. The analysis results confirmed that "Social Dilemma" showed the structure of a traditional current affairs documentary and mainly used experts and statistical data to develop the story. It also reinforced core content claims by enumerating domestic and foreign cases such as the 2021 Myanmar massacre and the spread of fake news. In addition, the relationship between the characters clearly revealed the binary opposition between developers and media companies as well as users and advertisers. For the solution to the problem, strong regulations on businesses and the suspension of social media use were reached. However, "The Social Dilemma" merely pointed out the misuse of AI technology and had a narrative that ignored human identity and social relationships. Such results raise the need for creating contents that emphasize the importance of human sociality, relationships, and learning ability in the age of AI.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.645-661
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• 2021

Scriptwriters have appeared in Korea's broadcasting production system for more than 40 years as a key producer. This study specifically investigated the work identity and labor experience of scriptwriters who have played countless roles from planning and organizing programs in various broadcast genres such as non-drama informative program, entertainment, news, and radio to script writing. As a result of examining the work identity and labor experience of the scriptwriters based on the auto-ethnography of the 20 scriptwriters working in the field, they felt that they had an " indispensable" program producer and a media culture producer and at the same time felt that they were taking on tasks that were unclear. They felt that the cause of this inequality was a problem of the production system and employment type, but they recognized that they could not be solved individually, and they were developing their own skills or building connections to get work, and expanding their areas unconditionally.

• Smart Media Journal
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• v.11 no.8
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• pp.73-83
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• 2022

In order to support new construction engineering companies with weak information power to successfully advance into the overseas construction market, this study tried to analyze what are the keywords of interest in the overseas construction market and how they differ from Korea. For this purpose, we recently collected 2,473 news article titles and major articles targeting smart cities that are of high interest in Korea and Vietnam. Through network configuration and topic modeling, we examined the connection relationship between the word of interest and the word of interest. In addition, the influence of the word of interest in the network was measured using PageRank centrality. Through this analysis, it was found that there is a high interest in smart city-related construction, cities, and digital in both countries, and the difference in terms of interest between Korea and Vietnam was inferred. Finally, the limitations of this study and additional research directions to complement them are presented.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.113-119
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• 2023

Despite the lifting of the mandatory wearing of masks, which was the main tool of the COVID-19 quarantine policy, we paid attention to the fact that some people are still wearing masks, and we wanted to clarify why people do not take off their masks. Through a survey in this regard, we were able to ascertain why some people continue to wear masks in a broader context. In this article, we directly and indirectly confirm the hidden side of citizens' continued wearing of masks by analyzing how the lifting of the mask-wearing obligation was reported in media articles that have a significant impact on citizens' behavior and attitude. Through this, it was confirmed that citizens continue to wear masks to protect themselves in an uncertain situation where the COVID-19 endemic has not been declared, despite the quarantine authorities' announcement of lifting the mandatory wearing. In a situation where crises such as COVID-19 are expected to repeat frequently in the future, it was concluded that it is important to build trust in the quarantine authorities.

• Smart Media Journal
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• v.12 no.5
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• pp.17-27
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• 2023

Recently, automatic text summarization, which automatically summarizes only meaningful information for users, is being studied steadily. Especially, research on text summarization using Transformer, an artificial neural network model, has been mainly conducted. Among various studies, the GSG method, which trains a model through sentence-by-sentence masking, has received the most attention. However, the traditional GSG has limitations in selecting a sentence to be masked based on the degree of overlap of tokens, not the meaning of a sentence. Therefore, in this study, in order to improve the quality of text summarization, we propose SbGSG (Semantic-based GSG) methodology that selects sentences to be masked by GSG considering the meaning of sentences. As a result of conducting an experiment using 370,000 news articles and 21,600 summaries and reports, it was confirmed that the proposed methodology, SbGSG, showed superior performance compared to the traditional GSG in terms of ROUGE and BERT Score.

• Smart Media Journal
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• v.12 no.11
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• pp.77-85
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• 2023

After COVID-19, communication through online platforms has increased, leading to an accumulation of massive amounts of conversational text data. With the growing importance of summarizing this text data to extract meaningful information, there has been active research on deep learning-based abstractive summarization. However, conversational data, compared to structured texts like news articles, often contains missing or transformed information, necessitating consideration from multiple perspectives due to its unique characteristics. In particular, vocabulary omissions and unrelated expressions in the conversation can hinder effective summarization. Therefore, in this study, we restructured by considering the characteristics of Korean conversational data, fine-tuning a pre-trained text summarization model based on KoBART, and improved conversation data summary perfomance through a refining operation to remove redundant elements from the summary. By restructuring the sentences based on the order of utterances and extracting a central speaker, we combined methods to restructure the conversation around them. As a result, there was about a 4 point improvement in the Rouge-1 score. This study has demonstrated the significance of our conversation restructuring approach, which considers the characteristics of dialogue, in enhancing Korean conversation summarization performance.

• Journal of the Korean Society for information Management
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• v.23 no.1 s.59
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• pp.99-119
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• 2006

The most widely used information searching method in the current internet environment is the keyword-based one, which has certain limitations in terms of precision and recall. Most major internet portals provide directory-based searching as a means to complement these limitations. However, that they adopt different classification schemes brings significant inconvenience to the users, and it consequently suggests a need to develop mapping gateway to provide cross-portal, or cross-directory information searching. In this context, this study attempts to develop a prototype system of intermediary gateway for integrated search, using the directory services of three major portals, Naver, Yahoo and Empas, and test its performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11a
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• pp.613-616
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• 2003

개인용 단말기의 급속한 확산으로 인해 언제, 어디서나 시간과 공간의 제약없이 웹 컨텐츠를 이용하고자 하는 욕구가 증대하고 있다. 그러나 현재 유선에서 지원되는 웹 컨텐츠를 개인용 단말기에서 지원하기에는 단말기의 성능상 한계(screen size, memory size, bandwidth 등) 때문에 여러 가지 문제가 있다. 트랜스코딩이란 이러한 기존 유선 환경에서 제공되는 웹 컨텐츠를 특정 환경에 적합한 형태로 변환하는 것을 의미한다. 그러나 이와 관련된 기존 연구에서는 사용자가 요구하는 사항만을 변환하거나 서비스 제공자가 일방적으로 변환하여 웹 컨텐츠를 제공하고 있다. 따라서 이슈변화에 따른 사용자의 대처능력이 떨어지기 때문에 사용자의 사용성이 저하되며, 사용자에게 무의미한 정보 제공의 가능성이 있다. 이러한 문제점들을 해결하기 위해 본 논문에서는 사용자 프로파일에 의한 요구사항과 서비스 제공자의 의견을 함께 제공할 수 있는 변환 기법을 제안하고, 특히 멀티미디어 뉴스 제작을 위한 표준인 NewsML을 대상으로 적용하였다. 사용자 프로파일에 의한 요구 사항은 XML 문서의 구조 정보를 이용하여 자동으로 추출하고, 서비스 제공자의 의견은 문서의 레이아웃(Layout) 정보를 가지고, 어노테이션(Annotation) 기법을 활용하여 수동으로 추출한다. 그 결과, 사용자 관점에 부합하는 변환이 이루어지고, 다양한 이슈변화에 대한 대처능력이 향상되어 사용자의 사용성이 증대되었다.