• Journal of KIISE:Computing Practices and Letters
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• v.16 no.1
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• pp.110-114
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• 2010

We describe a new method for text categorization using TextRank algorithm. Text categorization is a problem that over one pre-defined categories are assigned to a text document. TextRank algorithm is a graph-based ranking algorithm. If we consider that each word is a vertex, and co-occurrence of two adjacent words is a edge, we can get a graph from a document. After that, we find important words using TextRank algorithm from the graph and make feature which are pairs of words which are each important word and a word adjacent to the important word. We use classifiers: SVM, Na$\ddot{i}$ve Bayesian classifier, Maximum Entropy Model, and k-NN classifier. We use non-cross-posted version of 20 Newsgroups data set. In consequence, we had an improved performance in whole classifiers, and the result tells that is a possibility of TextRank algorithm in text categorization.

• Journal of KIISE
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• v.44 no.2
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• pp.179-185
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• 2017

Document summarization aims to generate a summary that is consistent and contains the highly related sentences in a document. In this study, we implemented for document summarization that extracts highly related sentences from a whole document by considering both similarities and entailment relations between sentences. Accordingly, we proposed a new algorithm, TextRank-NLI, which combines a Recurrent Neural Network based Natural Language Inference model and a Graph-based ranking algorithm used in single document extraction-based summarization task. In order to evaluate the performance of the new algorithm, we conducted experiments using the same datasets as used in TextRank algorithm. The results indicated that TextRank-NLI showed 2.3% improvement in performance, as compared to TextRank.

• Journal of Korea Multimedia Society
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• v.21 no.1
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• pp.45-50
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• 2018

This research paper describes how to summarize music lyrics using the TextRank algorithm. This method can summarize music lyrics as important lyrics. Therefore, we recommend music more effectively than analyzing the number of words and recommending music.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.467-474
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• 2018

To organize and document the contents of meetings and discussions is very important in various tasks. However, in the past, people had to manually organize the contents themselves. In this paper, we describe the development of a system that generates the meeting minutes automatically using the TextRank algorithm. The proposed system records all the utterances of the speaker in real time and calculates the similarity based on the appearance frequency of the sentences. Then, to create the meeting minutes, it extracts important words or phrases through a non-supervised learning algorithm for finding the relation between the sentences in the document data. Especially, we improved the performance by introducing the keyword weighting technique for the TextRank algorithm which reconfigured the PageRank algorithm to fit words and sentences.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.285-289
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• 2009

Tag is important to retrieve and classify contents. However, someone uses so many unrelated tags with contents for the high ranking In this work, we propose tag refinement algorithm using TextRank. We calculate the importance of keywords occurred a title, description, tag, and comments. We refine tags removing unrelated keywords from user generated tags. From the results of experiments, we can see that proposed method is useful for refining tags.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.509-517
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• 2023

The reactive web service provides a related word recommendation system using the TextRank algorithm and a word-based idea generation service selected by the user. In the related word recommendation system, the method of weighting each word using the TextRank algorithm and the probability output method using SoftMax are discussed. The idea generation service discusses the idea generation method and the artificial intelligence reinforce-learning method using mini-GPT. The reactive web discusses the linkage process between React, Spring Boot, and Flask, and describes the overall operation method. When the user enters the desired topic, it provides the associated word. The user constructs a mind map by selecting a related word or adding a desired word. When a user selects a word to combine from a constructed mind-map, it provides newly generated ideas and related patents. This web service can share generated ideas with other users, and improves artificial intelligence by receiving user feedback as a horoscope.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.1
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• pp.18-33
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• 2014

A Word is the smallest unit for text analysis, and the premise behind most text-mining algorithms is that the words in given documents can be perfectly recognized. However, the newly coined words, spelling and spacing errors, and domain adaptation problems make it difficult to recognize words correctly. To make matters worse, obtaining a sufficient amount of training data that can be used in any situation is not only unrealistic but also inefficient. Therefore, an automatical word extraction method which does not require a training process is desperately needed. WordRank, the most widely used unsupervised word extraction algorithm for Chinese and Japanese, shows a poor word extraction performance in Korean due to different language structures. In this paper, we first discuss why WordRank has a poor performance in Korean, and propose a customized WordRank algorithm for Korean, named KR-WordRank, by considering its linguistic characteristics and by improving the robustness to noise in text documents. Experiment results show that the performance of KR-WordRank is significantly better than that of the original WordRank in Korean. In addition, it is found that not only can our proposed algorithm extract proper words but also identify candidate keywords for an effective document summarization.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.62-66
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• 2016

문서의 의미 기반 처리를 위해서 문서의 내용을 대표하는 키워드를 추출하는 것은 정확성과 효율성 측면에서 매우 중요한 과정이다. 그러나 단일문서로부터 키워드를 추출해 내는 기존의 연구들은 정확도가 낮거나 한정된 분야에 대해서만 검증을 수행하여 결과를 신뢰하기 어려운 문제가 있었다. 따라서 본 연구에서는 정확하면서도 다양한 분야의 텍스트에 적용 가능한 키워드 추출 방법을 제시하고자 단어의 동시출현 정보와 그래프 모델을 바탕으로 TextRank 알고리즘을 변형한 새로운 형태의 알고리즘을 동시에 적용하는 키워드 추출 기법을 제안하였다. 제안한 기법을 활용하여 성능평가를 진행한 결과 기존의 연구들보다 향상된 정확도를 얻을 수 있음을 확인하였다.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.62-66
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• 2016

문서의 의미 기반 처리를 위해서 문서의 내용을 대표하는 키워드를 추출하는 것은 정확성과 효율성 측면에서 매우 중요한 과정이다. 그러나 단일문서로부터 키워드를 추출해 내는 기존의 연구들은 정확도가 낮거나 한정된 분야에 대해서만 검증을 수행하여 결과를 신뢰하기 어려운 문제가 있었다. 따라서 본 연구에서는 정확하면서도 다양한 분야의 텍스트에 적용 가능한 키워드 추출 방법을 제시하고자 단어의 동시출현정보와 그래프 모델을 바탕으로 TextRank 알고리즘을 변형한 새로운 형태의 알고리즘을 동시에 적용하는 키워드 추출 기법을 제안하였다. 제안한 기법을 활용하여 성능평가를 진행한 결과 기존의 연구들보다 향상된 정확도를 얻을 수 있음을 확인하였다.

• Journal of KIISE
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• v.44 no.11
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• pp.1236-1243
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• 2017

In this paper, we propose a novel way of producing keyword networks, named LSI-based ClusterTextRank, which extracts significant key words from a set of clusters with a mutual information metric, and constructs an association network using latent semantic indexing (LSI). The proposed method reduces the dimension of documents through LSI, decomposes documents into multiple clusters through k-means clustering, and expresses the words within each cluster as a maximal spanning tree graph. The significant key words are identified by evaluating their mutual information within clusters. Then, the method calculates the similarities between the extracted key words using the term-concept matrix, and the results are represented as a keyword association network. To evaluate the performance of the proposed method, we used travel-related blog data and showed that the proposed method outperforms the existing TextRank algorithm by about 14% in terms of accuracy.