• Title/Summary/Keyword: Paragraph Extraction

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Keyword Weight based Paragraph Extraction Algorithm (키워드 가중치 기반 문단 추출 알고리즘)

  • Lee, Jongwon;Joo, Sangwoong;Lee, Hyunju;Jung, Hoekyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2017.10a
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    • pp.504-505
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    • 2017
  • Existing morpheme analyzers classify the words used in writing documents. A system for extracting sentences and paragraphs based on a morpheme analyzer is being developed. However, there are very few systems that compress documents and extract important paragraphs. The algorithm proposed in this paper calculates the weights of the keyword written in the document and extracts the paragraphs containing the keyword. Users can reduce the time to understand the document by reading the paragraphs containing the keyword without reading the entire document. In addition, since the number of extracted paragraphs differs according to the number of keyword used in the search, the user can search various patterns compared to the existing system.

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XML Document Keyword Weight Analysis based Paragraph Extraction Model (XML 문서 키워드 가중치 분석 기반 문단 추출 모델)

  • Lee, Jongwon;Kang, Inshik;Jung, Hoekyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.11
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    • pp.2133-2138
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    • 2017
  • The analysis of existing XML documents and other documents was centered on words. It can be implemented using a morpheme analyzer, but it can classify many words in the document and cannot grasp the core contents of the document. In order for a user to efficiently understand a document, a paragraph containing a main word must be extracted and presented to the user. The proposed system retrieves keyword in the normalized XML document. Then, the user extracts the paragraphs containing the keyword inputted for searching and displays them to the user. In addition, the frequency and weight of the keyword used in the search are informed to the user, and the order of the extracted paragraphs and the redundancy elimination function are minimized so that the user can understand the document. The proposed system can minimize the time and effort required to understand the document by allowing the user to understand the document without reading the whole document.

Deep Learning Document Analysis System Based on Keyword Frequency and Section Centrality Analysis

  • Lee, Jongwon;Wu, Guanchen;Jung, Hoekyung
    • Journal of information and communication convergence engineering
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    • v.19 no.1
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    • pp.48-53
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    • 2021
  • Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

Keyword Analysis Based Document Compression System

  • Cao, Kerang;Lee, Jongwon;Jung, Hoekyung
    • Journal of information and communication convergence engineering
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    • v.16 no.1
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    • pp.48-51
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    • 2018
  • The traditional documents analysis was centered on words based system was implemented using a morpheme analyzer. These traditional systems can classify used words in the document but, cannot help to user's document understanding or analysis. In this problem solved, System needs extract for most valuable paragraphs what can help to user understanding documents. In this paper, we propose system extracts paragraphs of normalized XML document. User insert to system what filename when wants for analyze XML document. Then, system is search for keyword of the document. And system shows results searched keyword. When user choice and inserts keyword for user wants then, extracting for paragraph including keyword. After extracting paragraph, system operating maintenance paragraph sequence and check duplication. If exist duplication then, system deletes paragraph of duplication. And system informs result to user what counting each keyword frequency and weight to user, sorted paragraphs.

Keyword Weight based Paragraph Extraction Algorithm (문단 가중치 분석 기반 본문 영역 선정 알고리즘)

  • Lee, Jongwon;Yu, Seongjong;Kim, Doan;Jung, Hoekyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2018.05a
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    • pp.462-463
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    • 2018
  • Traditional document analysis systems used word-based analysis using a morphological analyzer or TF-IDF technique. These systems have the advantage of being able to derive key keywords by calculating the weights of the keywords. On the other hand, it is not appropriate to analyze the contents of documents due to the structural limitations. To solve this problem, the proposed algorithm calculates the weights of the documents in the document and divides the paragraphs into areas. And we calculate the importance of the divided regions and let the user know the area with the most important paragraphs in the document. So, it is expected that the user will be provided with a service suitable for analyzing documents rather than using existing document analysis systems.

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Document Analysis based Main Requisite Extraction System (문서 분석 기반 주요 요소 추출 시스템)

  • Lee, Jongwon;Yeo, Ilyeon;Jung, Hoekyung
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.4
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    • pp.401-406
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    • 2019
  • In this paper, we propose a system for analyzing documents in XML format and in reports. The system extracts the paper or reports of keywords, shows them to the user, and then extracts the paragraphs containing the keywords by inputting the keywords that the user wants to search within the document. The system checks the frequency of keywords entered by the user, calculates weights, and removes paragraphs containing only keywords with the lowest weight. Also, we divide the refined paragraphs into 10 regions, calculate the importance of the paragraphs per region, compare the importance of each region, and inform the user of the main region having the highest importance. With these features, the proposed system can provide the main paragraphs with higher compression ratio than analyzing the papers or reports using the existing document analysis system. This will reduce the time required to understand the document.