• ETRI Journal
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• v.27 no.6
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• pp.806-809
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• 2005

The main goal of this research is automatic video analysis using a face recognition technique. In this paper, an automatic video management system is introduced with a variety of functions enabled, such as index, edit, summarize, and retrieve multimedia data. The automatic management tool utilizes MPEG-7 visual descriptors to generate a video index for creating a summary. The resulting index generates a preview of a movie, and allows non-linear access with thumbnails. In addition, the index supports the searching of shots similar to a desired one within saved video sequences. Moreover, a face recognition technique is utilized to personalbased video summarization and indexing in stored video data.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.493-495
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• 2003

다중문서요약에서는 단일문서요약과 달리 문장간의 중복도를 측정하는 방법이 요구된다. 기존에는 중복된 단어의 빈도수를 이용하거나, 구문트리 구조를 이용한 방법이 있으나, 중복도를 측정하는데 도움이 되지 못하는 단어와, 구문분석기 성능에 따라서 중복도 측정에 오류를 발생시킨다. 본 논문은 주절 종속절의 구분, 문장성분, 주절 용언의 의미를 이용하는 문장간 중복도 측정방법을 제안한다. 위의 방법으로 구현된 시스템은 기존의 중복된 단어 빈도수 방식에 비해 정확율에서 56%의 성능 향상이 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.898-900
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• 2004

시간에 중속적인 문서집합에서 사람이 만든 요약문은 시간에 따른 중요 내용의 분포를 보여준다. 본 논문은 다중 문서에 시간 자질을 이용한 문서의 분류와 시간별 문서집합에서 핵심문장과 부가문장을 선별하고, 문장간의 계층적인 클러스터링을 통해서 중요 문장을 선별하는 방법을 제안한다. 동일한 주제를 갖는 문서집합에서 사랑이 선택한 중요 문장에 대해서 제안한 방법은 50% 정확률을 나타냈다.

• The KIPS Transactions:PartB
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• v.12B no.6 s.102
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• pp.691-696
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• 2005

In this paper, we present a pilot system that tn compress a Korean sentence automatically using knowledge extracted from news articles and their headlines. A sot of compressed sentences can be presented as an abstraction of a document. As a compressed sentence is of headline-style, it could be easily displayed on small devices, such as mobile phones and other handhold devices. Our compressing system has shown to be promising through a preliminary experiment.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.752-777
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• 2017

This paper proposes an automatic method to summarize Bangla news document. In the proposed approach, pronoun replacement is accomplished for the first time to minimize the dangling pronoun from summary. After replacing pronoun, sentences are ranked using term frequency, sentence frequency, numerical figures and title words. If two sentences have at least 60% cosine similarity, the frequency of the larger sentence is increased, and the smaller sentence is removed to eliminate redundancy. Moreover, the first sentence is included in summary always if it contains any title word. In Bangla text, numerical figures can be presented both in words and digits with a variety of forms. All these forms are identified to assess the importance of sentences. We have used the rule-based system in this approach with hidden Markov model and Markov chain model. To explore the rules, we have analyzed 3,000 Bangla news documents and studied some Bangla grammar books. A series of experiments are performed on 200 Bangla news documents and 600 summaries (3 summaries are for each document). The evaluation results demonstrate the effectiveness of the proposed technique over the four latest methods.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.4
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• pp.149-154
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• 2014

A user should pick up relevant answers by himself from various search results when using user participation question answering community like Knowledge-iN. If refined answers are automatically provided, usability of question answering community must be improved. This paper divides questions in Q&A documents into 4 types(word, list, graph and text), then proposes summarizing methods for each question type using document statistics. Summarized answers for word, list and text type are obtained by question clustering and calculating scores for words using frequency, proximity and confidence of answers. Answers for graph type is shown by extracting user opinion from answers.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.681-686
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• 2003

In this paper, we describes a system that extracts important sentences from Korean newspaper articles using automatic paragraphing. First, we detect repeated words between sentences. Through observation of the repeated words, this system compute Closeness Degree between Sentences(CDS ) from the degree of morphological agreement and the change of grammatical role. And then, it automatically divides a document into meaningful paragraphs using the number of paragraph defined by the user´s need. Finally. it selects one representative sentence from each paragraph and it generates summary using representative sentences. Though our system doesn´t utilize some features such as title, sentence position, rhetorical structure, etc., it is able to extract meaningful sentences to be included in the summary.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.1
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• pp.64-75
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• 2024

With the development of the Internet, the information in our lives has become fast and diverse. Especially in the field of defense, articles and information are pouring in from various sources every day, and fast information selection, understanding, and decision-making are required in the ever-changing situation. It is very cumbersome to go from platform to platform and read articles one by one to get the information you need. To solve this problem, this research aims to save time and provide quick access to the latest information by allowing you to quickly grasp key information from summarized content without having to read the entire article. This can improve efficiency by allowing defense professionals to focus more on important tasks rather than extensive information search and analysis.

• Korean Journal of Cognitive Science
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• v.12 no.1_2
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• pp.35-42
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• 2001

In this paper. a document is represented as a weighted graph called a text relationship map. In the graph. a node represents a vector of nouns in a sentence, an edge completely connects other nodes. and a weight on the edge is a value of the similarity between two nodes. The similarity is based on the word overlap between the corresponding nodes. The importance of a node. called an aggregate similarity in this paper. is defined as the sum of weights on the links connecting it to other nodes on the map. In this paper. we present a Korean text summarization system using the aggregate similarity. To evaluate our system, we used two test collection, one collection (PAPER-InCon) consists of 100 papers in the field of computer science: the other collection (NEWS) is composed of 105 articles in the newspapers and had built by KOROlC. Under the compression rate of 20%. we achieved the recall of 46.6% (PAPER-InCon) and 30.5% (NEWS) and the precision of 76.9% (PAPER-InCon) and 42.3% (NEWS).

• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.227-243
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• 2008

This study investigates an event detection method with the aim of generating an event-focused news summary from a set of news articles on a certain event using a multi-document summarization technique. The event detection method first classifies news articles into the event related topic categories by employing a SVM classifier and then creates event clusters containing news articles on an event by a modified single pass clustering algorithm. The clustering algorithm applies a time penalty function as well as cluster partitioning to enhance the clustering performance. It was found that the event detection method proposed in this study showed a satisfactory performance in terms of both the F-measure and the detection cost.