• The Journal of Natural Sciences
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• v.8 no.1
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• pp.109-114
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• 1995

For effective management of statistical database, statistical summary information must be provided by accessing directly the precomputed summary data from summary database to store and manage meta database for supporting statistical analysis and providing users with statistical summary information. In order to support effectively the use of summary database, we do the design and implementation of meta database and manager having a hierarchical structure as a data dictionary/directory and operation method is presented.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.35-44
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• 2021

Previous studies have studied short videos, short form content, snack culture and so on, but few studies have been conducted on the form of summary content that compressing and summarizing the original content. This study aims to contribute to the revitalization of the summary content market by exploring ways to enhance user satisfaction through analysis of the YouTube summary content features and the credibility of content providers that bring about flow and satisfaction of YouTube summary content users. The survey was conducted on 202 people who have watched YouTube summary contents for finding out the effects of YouTube summary contents features and content provider credibility on the details of flow. As a result, only entertainment had a significant impact on all flow details. This study is of academic significance in that it defines the features of YouTube summary contents, and has practical significance in that it suggests what direction the summary content should have in order to arouse user satisfaction in future.

• ETRI Journal
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• v.43 no.2
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• pp.288-298
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• 2021

Abstractive text summarization is a process of making a summary of a given text by paraphrasing the facts of the text while keeping the meaning intact. The manmade summary generation process is laborious and time-consuming. We present here a summary generation model that is based on multilayered attentional peephole convolutional long short-term memory (MAPCoL; LSTM) in order to extract abstractive summaries of large text in an automated manner. We added the concept of attention in a peephole convolutional LSTM to improve the overall quality of a summary by giving weights to important parts of the source text during training. We evaluated the performance with regard to semantic coherence of our MAPCoL model over a popular dataset named CNN/Daily Mail, and found that MAPCoL outperformed other traditional LSTM-based models. We found improvements in the performance of MAPCoL in different internal settings when compared to state-of-the-art models of abstractive text summarization.

• Journal of Distribution Research
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• v.9 no.2
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• pp.101-122
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• 2004

In this study, two alternative models are developed and tested in order to investigate the relationship between retailer image and its private brand attitude. The halo effect model hypothesizes that retailer image is related as a halo in private brand evaluation and the summary construct model hypothesizes that retailer image functions as a summary construct of private brand evaluation. The results indicate there are moderating effects of 1) familiarity with a private brand and 2) the characteristics of a product category High familiarity is related with the summary construct model and low familiarity the halo effect model. In private brand food, the summary construct model fits better and explains more adequately that private brand evaluation influences retailer image as a summary construct. In private brand clothes, however, the halo effect model performs better in explaining the relationship between retailer image and private brand attitude.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.

• The Journal of Information Technology and Database
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• v.4 no.2
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• pp.33-45
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• 1998

Database summarization is the procedure to obtain generalized and representative descriptions expressing the content of a large amount of database at a glance. We present a top-down summary refinement procedure to discover database summaries. The procedure exploits attribute concept hierarchies that represent ISA relationships among domain concepts. It begins with the most generalized summary and proceeds to find more specialized ones by stepwise refinements. This top-down paradigm reveals at least two important advantages compared to the previous bottom-up methods. Firstly, it provides a natural way of reflecting the user's own discovery preference interactively. Secondly, it does not produce too large intermediate result that makes it hard for the bottom-up approach to be applied in practical environment. The proposed procedure can also be easily extended for distributed databases. Information content measure of a database summary is derived in order to identify more informative summaries among the discovered results.

• Journal of Korean Library and Information Science Society
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• v.49 no.3
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• pp.111-132
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• 2018

The purpose of this study was to identify the structure and pattern of conclusions chapters in research articles selected in four representative journals published in Korean LIS. To analyse the structure and pattern five categories and eleven elements drawn from RA conclusions were used such as 'Drawing Attention', 'Summarizing', 'Discussing', 'Applying' and 'Extending'. The Findings are as follows. 'Conclusions' was the most used title of the chapter, and then 'conclusions and suggestions'. The conclusions sections were consisted averagely 4.2 elements. 'Summary of findings' was the most frequently elements, followed by 'summary of research process', 'suggestion of further research' and 'implication'. Also, 'summary of findings' was the most used in lengths. 'Summarizing' was the highest frequency in five categories, followed by 'Extending', and 'Applying'. The dominant first element begun conclusions sections was 'summary of findings' and the final element closed conclusion was 'suggestion of further research'. 'Summary of research process - summary of findings' order was found in the most frequent pattern of conclusions. 'Summary of findings' and 'summary of research processes' in 'Summarizing' were found as essential functions, while other elements, such as significance, 'application', 'limitations' in 'Applying' and 'Extending' were found as optional functions in the conclusions of Korean LIS research articles.

• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.3-14
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• 2006

Music summarization means a technique which automatically generates the most importantand representative a part or parts ill music content. The techniques of music summarization have been studied with two categories according to summary characteristics. The first one is that the repeated part is provided as music summary and the second provides the combined segments which consist of segments with different characteristics as music summary in music content In this paper, we propose and evaluate two kinds of music summarization techniques. The algorithm using multi-level vector quantization which provides a repeated part as music summary gives fixed-length music summary is evaluated by overlapping ration between hand-made repeated parts and automatically generated summary. As results, the overlapping ratios of conventional methods are 42.2% and 47.4%, but that of proposed method with fixed-length summary is 67.1%. Optimal length music summary is evaluated by the portion of overlapping between summary and repeated part which is different length according to music content and the result shows that automatically-generated summary expresses more effective part than fixed-length summary with optimal length. The cluster-based algorithm using 2-D similarity matrix and k-means algorithm provides the combined segments as music summary. In order to evaluate this algorithm, we use MOS test consisting of two questions(How many similar segments are in summarized music? How many segments are included in same structure?) and the results show good performance.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.488-497
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• 2004

This paper describes the hybrid document summarization using the indicative summarization and the query-based summarization. The learning models are built from teaming documents in order to extract topic phrases. We use Naive Bayesian, Decision Tree and Supported Vector Machine as the machine learning algorithm. The system extracts topic phrases automatically from new document based on these models and outputs the summary of the document using query-based summarization which considers the extracted topic phrases as queries and calculates the locality-based similarity of each topic phrase. We examine how the topic phrases affect the summarization and how many phrases are proper to summarization. Then, we evaluate the extracted summary by comparing with manual summary, and we also compare our summarization system with summarization mettled from MS-Word.

• Journal of Digital Contents Society
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• v.19 no.2
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• pp.301-307
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• 2018

Generally, search engine summarizes the main contents of the search results so that user can click the site providing the information of the contents to search first. In this study, we demonstrated whether the user clicks on the search results based on the summary content provided by the search engine or on the order of the search result placement through empirical studies through the presentation of search results. By using the API provided by the search engine company, a search site that presents the search results in a regular and inverse order is created, and the click action of each user's search result is displayed in the order of actual click order, click position, and the user's search type such as the route of movement. As a result of the analysis, most users account for more than 60% of users who click on the first and second exposed search results regardless of the search results. It is confirmed that the search priority of users is determined according to the order of search results regardless of the summary of search results.