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Session Ⅱ : Artificial Intelligence

AI-guided Story Generation Framework with Automatic Storyline Generator

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 8th International Conference on Next Generation Computing 2022 (2022.10)바로가기
  • 페이지
    pp.57-60
  • 저자
    Juntae Kim, Yoonseok Heo, Inkwon Lee, Jungwook Han, Jongho Nang
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A419738

원문정보

초록

영어
A story generation task is to develop a system that can continuously generate natural, consistent, and coherent stories for consecutive scenes. Recently transformer-based language models have shown considerable results at the sentence-level generation for learning human-writing ability. However, it is very crucial to understand the way of developing the story using a combination of various contents. Recent works have mainly focused on human-guided AI story generation methods in which humans as guidance determine the next storyline, and the system creates a story that reflects the storyline well. This study focuses on the way of replacing the human role with the AI-based model. Based on this, this study deals with the methodology for creating a long story spanning multiple scenes rather than creating a story at the level of one scene. In this regard, we propose a novel AI-guided story generation framework with automatic storyline generator. It is a pipeline structure consisting of two modules such as a storyline generator and a story generator, which enables the continuous creation of coherent stories. Particularly, we transform the storyline generation problem into a multiple-choice QA problem to predict the next storyline. This study shows the possibility of generating continuous stories for multiple scenes without any human intervention.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Human-guided story generation approaches
B. Outline-conditioned Story Generation Approach
III. PROPOSED ARCHITECTURE
A. Storyline Generation
B. Story Generation
IV. EXPERIMENT
A. Datasets
B. Result
V. CONCLUSIONS
REFERENCES

키워드

Story Generation Storyline Generation Human-guided AI Deep Learning Natural Language Generation

저자

  • Juntae Kim [ Computer Science and Engineering Sogang University ]
  • Yoonseok Heo [ Computer Science and Engineering Sogang University ]
  • Inkwon Lee [ Computer Science and Engineering Sogang University ]
  • Jungwook Han [ Computer Science and Engineering Sogang University ]
  • Jongho Nang [ Computer Science and Engineering Sogang University ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

이 권호 내 다른 논문 / 한국차세대컴퓨팅학회 학술대회 The 8th International Conference on Next Generation Computing 2022

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