• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.43-50
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• 2004

The Statistic-based method is widely used for automatic single document summarization in large sets of documents such as those on the web. However, the results of this method shows high redundancies in the summarized sentences because this method selects sentences including words that frequently appear in the document. We solve this problem using the method MMR to raise the quality of document summary (The best results are appeared around λ=0.6). Also, we compare the MMR summaries with those done by human subjects and verify their accuracy.

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1319-1322
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• 2003

We present a web document summarizer, simpler more condense than the existing ones, of a search engine. This summarizer generates summaries with a statistic-based summarization method using Clustering or MMR technique to reduce redundancy in the results, and that generates summaries using Perfect Link Graph. We compare the results with the summaries generated by human subjects. For the comparison, we use FScore. Our experimental results verify the accuracy of the summarization methods.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.2
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• pp.26-31
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• 2005

In this paper, we present the document summarizers which are simpler and more condense than the existing ones generally used in the web search engines. This method is a statistic-based summarization method using the concept of the complete graph. We suppose that each sentence as a vertex and the similarity between two sentences as a link of the graph. We compare this summarizer with those of Clustering and MMR techniques which are well-known as the good summarization methods. For the comparison, we use FScore using the summarization results generated by human subjects. Our experimental results verify the accuracy of this method, being about $30\%$ better than the others.

• The Mathematical Education
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• v.44 no.3 s.110
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• pp.457-475
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• 2005

We have inquired on what the statistical classes of the secondary schools had been aiming to, say the epistermlogical objects. And we now appreciate that the main obstacle to the systematic articulation is the lack of anticipation on what the statistical concepts are. This study focuses on the ingredients of the statistical concepts. Those are to be the ground of the systematic articulation of statistic courses, especially of the one for the school kids. Thus we required that those ingredients must satisfy the followings. i) directly related to the contents of statistics ii) psychologically developing iii) mutually exclusive each other as much as possible iv) exhaustive enough to cover all statistical concepts We examined what and how statisticians had been doing and the various previous views on these. After all we suggest the following three concepts are the core of conceptual developments of statistic, say the concept of distributions, the summarizing ability and the concept of samples. By the concepts of distributions we mean the frequency views on each random categories and that is developing from the count through the probability along ages. Summarizing ability is another important resources to embed his probe with the data set. It is not only viewed as a number but also to be anticipated as one reflecting a random phenomena. Inductive generalization is one of the most hazardous thing. Statistical induction is a scientific way of challenging this and this starts from distinguishing the chance with the inevitable consequences. One's inductive logic grows up along with one's deductive arguments, nevertheless they are different. The concept of samples reflects' one's view on the sample data and the way of compounding one's logic with the data within one's hypothesis. With these three in mind we observed Korean Statistic Curriculum from K to 12. Distributional concepts are dealt with throughout but not sequenced well. The way of summarization has been introduced in the 1 st, 5th, 7th and the 10th grade as a numerical value only. One activity on the concept of sample is given at the 6th grade. And it jumps into the statistical reasoning at the selective courses of ' Mathematics I ' or of ' Probability and Statistics ' in the grades of 11-12. We want to suggest further studies on the developing stages of these three conceptual features so as to obtain a firm basis of successive statistical articulation.