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Document Summarization Using TextRank Based on Sentence Embedding
Seok-won Jeong, Jintae Kim, Harksoo Kim
http://doi.org/10.5626/JOK.2019.46.3.285
Document summarization is creating a short version document that maintains the main content of original document. An extractive summarization has been actively studied by the reason of it guarantees the basic level of grammar and high level of accuracy by copying a large amount of text from the original document. It is difficult to consider the meaning of sentences because the TextRank, which is a typical extractive summarization method, calculates an edge of graph through the frequency of words. In a bid to solve these drawbacks, we propose a new TextRank using sentence embedding. Through experiments, we confirmed that the proposed method can consider the meaning of the sentence better than the existing method.
Construction of Korean Knowledge Base Based on Machine Learning from Wikipedia
Seok-won Jeong, Maengsik Choi, Harksoo Kim
The performance of many natural language processing applications depends on the knowledge base as a major resource. WordNet, YAGO, Cyc, and BabelNet have been extensively used as knowledge bases in English. In this paper, we propose a method to construct a YAGO-style knowledge base automatically for Korean (hereafter, K-YAGO) from Wikipedia and YAGO. The proposed system constructs an initial K-YAGO simply by matching YAGO to info-boxes in Wikipedia. Then, the initial K-YAGO is expanded through the use of a machine learning technique. Experiments with the initial K-YAGO shows that the proposed system has a precision of 0.9642. In the experiments with the expanded part of K-YAGO, an accuracy of 0.9468 was achieved with an average macro F1-measure of 0.7596.
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