Earticle

초록

영어

In this study, we discuss the basic technology of Text Summarization based on the deep neural network for natural language processing(NLP). The text summarization task is divided into an extractive summary and an abstractive summary. The extractive summary is a method of extracting a summary of the words used in the input document in the output text, and the abstractive summary is a problem of understanding the input statement and generating a sentence of the same content. The abstractive sentence generation system is based on the encoder-decoder model with attention mechanism, and a selector that can select input sentence is added. The Copy network and Pointer network are the special mechanisms for selector. Such selector systems can make text summarization to be the hybrid form of abstractive and extractive summary. In the future, we expect that accuracy of text summarization will be improved by adding reinforcement learning method.

목차

1. 서론
 2. 문서요약
  2.1 문서요약의 종류
  2.2 추출요약(Extractive Summary) 방식
  2.3 생성요약(Abstractive Summary) 방식
  2.4 문서요약의 성능측정지표
 3. 신경망 방식의 자연어처리 기술
  3.1 부호기-복호기 모델(Encoder-Decoder Model)
  3.2 순환 신경망
  3.3 컨콜루션 신경망(Convolutional Neural Network: CNN)
  3.4 RNN 기반의 부호기_복호기 기계번역
 4. 신경망 방식의 문서요약
  4.1 신경망 방식 생성요약의 도전과제
  4.2 생성요약 모델
 5. 결론
 인용문헌
 [Abstract]

저자

• 지인영 [ In-Young Jhee | 한국체육대학교 교양학부 ] 제1저자
• 김희동 [ Hee-Dong Kim | 한국외국어대학교 정보통신공학과 ] 제2저자

참고문헌