• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.50-55
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• 2016

To attain an understanding of customers' opinions or demands regarding a companies' products or service, it is important to consider VOC (Voice of Customer) data; however, it is difficult to understand contexts from VOC because segmented and duplicate sentences and a variety of dialog contexts. In this article, POS (part of speech) and morphemes were selected as language resources due to their semantic importance regarding documents, and based on these, we defined an LSP (Lexico-Semantic-Pattern) to understand the structure and semantics of the sentences and extracted summary by key sentences; furthermore the LSP was introduced to connect the segmented sentences and remove any contextual repetition. We also defined the LSP by categories and classified the documents based on those categories that comprise the main sentences matched by LSP. In the experiment, we classified the VOC-data documents for the creation of a summarization before comparing the result with the previous methodologies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.161-167
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• 2019

Recently, the amount of created information has been rising rapidly by dissemination of state of the art and developing of the various web service based on ICT. In additionally, the user has to need a lot of times and effort to find the necessary information which is the user want to know it in the mount of information. Document summarization is the technique that making and providing the summary of given document efficiently by analyzing and extracting the key sentences and words. However, it is hard to apply the previous of word embedding technique to the document which is composed by korean language for analyzing contents in the document due to the character of language. In this paper, we propose the new query-focused korean document summarization by exploiting word embedding technique such as Word2Vec and FastText, and then compare the both result of performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1769-1776
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• 2022

Automatically summarizing long sentences is an important technique. The BART model is one of the widely used models in the summarization task. In general, in order to generate a summarization model of a specific domain, fine-tuning is performed by re-training a language model trained on a large dataset to fit the domain. The fine-tuning is usually done by changing the number of nodes in the last fully connected layer. However, in this paper, we propose a fine-tuning method by adding an attention layer, which has been recently applied to various models and shows good performance. In order to evaluate the performance of the proposed method, various experiments were conducted, such as accumulating layers deeper, fine-tuning without skip connections during the fine tuning process, and so on. As a result, the BART model using two attention layers with skip connection shows the best score.

• Smart Media Journal
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• v.6 no.1
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• pp.22-29
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• 2017

Due to the rapid advancement and distribution of smart devices of late, document data on the Internet is on the sharp increase. The increment of information on the Web including a massive amount of documents makes it increasingly difficult for users to understand corresponding data. In order to efficiently summarize documents in the field of automated summary programs, various researches are under way. This study uses TextRank algorithm to efficiently summarize documents. TextRank algorithm expresses sentences or keywords in the form of a graph and understands the importance of sentences by using its vertices and edges to understand semantic relations between vocabulary and sentence. It extracts high-ranking keywords and based on keywords, it extracts important sentences. To extract important sentences, the algorithm first groups vocabulary. Grouping vocabulary is done using a scale of specific weight. The program sorts out sentences with higher scores on the weight scale, and based on selected sentences, it extracts important sentences to summarize the document. This study proved that this process confirmed an improved performance than summary methods shown in previous researches and that the algorithm can more efficiently summarize documents.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.72-74
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• 2022

The National Science and Technology Knowledge Information Service (NTIS) provides information on national R&D projects. Project information consists of meta-information such as 'project name', 'project performance institution', 'research manager name', and text explaining projects such as 'research goal', 'research content', and 'expected effect'. There is a problem that it takes a lot of time to find the desired project information by checking all of the "research goals" or "research contents" in the list of results of searching for 1 million project information. To solve this problem, this paper proposes a project information summary system that summarizes the parts consisting of long texts within the national R&D project information. By analyzing the linguistic characteristics of the Korean language, a preprocessor was built and a project information summary model based on natural language processing technology was developed to process preprocessed text information. Through this, project information composed of long sentences is provided in a compressed and summarized form, which will help users to easily and quickly infer the overall content with the summary information alone.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.238-244
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• 2000

본 논문에서 문서는 문서관계도라고 하는 그래프로 표현된다. 노드는 문서의 구성요소인 문장을 표현하고, 링크는 노드들 간의 의미적인 관계를 나타낸다 의미적 관계는 유사도에 의해서 결정되며, 문장의 중요도는 도합유사도로 나타낸다. 도합유사도는 한 노드와 인접한 노드들 사이의 유사도 합을 말한다. 본 논문에서는 도합유사도를 이용한 한국어 문서 기법을 제안한다. 실험에 사용된 평가용 요약문서는 정보처리 관련 분야에서 수집된 논문 100편과 KORDIC에서 구축한 신문기사 105건을 이용하였다. 문서 시스템에 의해서 생성된 문서의 크기가 본문의 20%이고, 본문이 논문(서론과 결론)일 경우, 재현율과 정확률은 각각 46.6%와 76.9%를 보였으며, 또한 본문이 신문기사일 경우, 재현율과 정확률은 각각 30.5%과 42.3%를 보였다. 또한 제안된 방법은 상용시스템보다 좋은 성능을 보였다.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.93-98
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• 2010

In this paper, we introduce a system that summarizes product evaluation through linguistic analysis to effectively utilize explosively increasing product reviews. Our system analyzes polarities of product reviews by product features, based on which customers evaluate each product like 'design' and 'material' for a skirt product category. The system shows to customers a graph as a review summary that represents percentages of positive and negative reviews. We build an opinion word dictionary for each product feature through context based automatic expansion with small seed words, and judge polarity of reviews by product features with the extracted dictionary. In experiment using product reviews from online shopping malls, our system shows average accuracy of 69.8% in extracting judgemental word dictionary and 81.8% in polarity resolution for each sentence.

• Journal of the Korea Computer Graphics Society
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• v.19 no.2
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• pp.9-18
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• 2013

We propose a novel method to extract keyframes from home videos based on the 5W and 1H information. Keyframe extraction is a kind of video summarization which selects only specific frames containing important information of a video. As a home video may have content with a variety of topics, we cannot make specific assumptions for information extraction. In addition, to summarize a home video we must analyze human behaviors, because people are important subjects in home videos. In this paper, we extract 5W and 1H information by analyzing human faces, human behaviors, and the global information of background. Experimental results demonstrate that our technique extract more similar keyframes to human selections than previous methods.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.87-96
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• 2006

Selectivity estimation is one of the query optimization techniques. It is difficult for the previous selectivity estimation techniques for moving objects to apply the position change of moving objects to synopsis. Therefore, they result in much error when estimating selectivity for queries, because they are based on the extended spatial synopsis which does not consider the property of the moving objects. In order to reduce the estimation error, the existing techniques should often rebuild the synopsis. Consequently problem occurs, that is, the whole database should be read frequently. In this paper, we proposed a moving object histogram method based on quad tree to develop a selectivity estimation technique for moving object queries. We then analyzed the performance of the proposed method through the implementation and evaluation of the proposed method. Our method can be used in various location management systems such as vehicle location tracking systems, location based services, telematics services, emergency rescue service, etc in which the location information of moving objects changes over time.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.613-620
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• 2008

Noun extraction plays an important part in the fields of information retrieval, text summarization, and so on. In this paper, we present a Korean base-noun extraction system and apply it to text summarization to deal with a huge amount of text effectively. The base-noun is an atomic noun but not a compound noun and we use tow techniques, filtering and segmenting. The filtering technique is used for removing non-nominal words from text before extracting base-nouns and the segmenting technique is employed for separating a particle from a nominal and for dividing a compound noun into base-nouns. We have shown that both of the recall and the precision of the proposed system are about 89% on the average under experimental conditions of ETRI corpus. The proposed system has applied to Korean text summarization system and is shown satisfactory results.