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Automated News Generation for TV Program Ratings

Authors:

Soomin Kim,

JongHwan Oh,

Joonhwan LeeAuthors Info & Claims

TVX '16: Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video

Pages 141 - 145

https://doi.org/10.1145/2932206.2933561

Published: 17 June 2016 Publication History

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Abstract

Automated journalism, automatically generating stories based on algorithms, has received considerable critical attention in diverse fields. However, automated journalism has not addressed the TV industry in much detail. This research aims to create a system to automatically generate news about TV ratings. The framework will involve undergoing the processes of data gathering, identifying important events by predefined algorithms, generating a story in narrative format, and publishing the output. The algorithm that determines the structure of the stories is defined by analyzing existing news about TV ratings that reflects key variables. Although the output of the research is limited to one type of news template, further attempts could expand to various formats.

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Cited By

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Alonso del Barrio DTiel MGatica-Perez D(2024)Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV ShowsProceedings of the 2024 ACM International Conference on Interactive Media Experiences10.1145/3639701.3656308(157-167)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3639701.3656308
Yuan LWang JYu LZhang X(2022)Hierarchical template transformer for fine-grained sentiment controllable generationInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10304859:5Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.103048
Strengers YQu LXu QKnibbe JBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Adhering, Steering, and Queering: Treatment of Gender in Natural Language GenerationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376315(1-14)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376315
Show More Cited By

Index Terms

Automated News Generation for TV Program Ratings
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Social and professional topics
  1. Professional topics
    1. Computing and business
      1. Automation

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Information

Published In

TVX '16: Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video

June 2016

202 pages

ISBN:9781450340670

DOI:10.1145/2932206

General Chairs:
Patrick Whitney
IIT Institute of Design, USA
,
Janet Murray
Ivan Allen College of Liberal Arts, Georgia Institute of Technology, USA
,
Santosh Basapur
IIT Institute of Design, USA
,
Program Chairs:
Noor Ali Hasan
Google Inc., USA
,
Jochen Huber
Synaptics Inc., Switzerland

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2016

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Work in progress

Funding Sources

CPRC program of MSIP/IITP

Conference

TVX'16

Sponsor:

SIGCHI

TVX'16: ACM International Conference on Interactive Experiences for TV and Online Video

June 22 - 24, 2016

Illinois, Chicago, USA

Acceptance Rates

TVX '16 Paper Acceptance Rate 12 of 38 submissions, 32%;

Overall Acceptance Rate 69 of 245 submissions, 28%

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Cited By

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Alonso del Barrio DTiel MGatica-Perez D(2024)Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV ShowsProceedings of the 2024 ACM International Conference on Interactive Media Experiences10.1145/3639701.3656308(157-167)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1145/3639701.3656308
Yuan LWang JYu LZhang X(2022)Hierarchical template transformer for fine-grained sentiment controllable generationInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10304859:5Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.103048
Strengers YQu LXu QKnibbe JBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Adhering, Steering, and Queering: Treatment of Gender in Natural Language GenerationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376315(1-14)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376315
Wu Y(2019)Is Automated Journalistic Writing Less Biased? An Experimental Test of Auto-Written and Human-Written News StoriesJournalism Practice10.1080/17512786.2019.168294014:8(1008-1028)Online publication date: 29-Oct-2019
https://doi.org/10.1080/17512786.2019.1682940

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Understanding User Perception of Automated News Generation System

Finding and expressing news from structured data

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