Research on News Production Strategy Under Algorithm

DOI

10.2991/assehr.k.220131.141How to use a DOI?

Keywords

news production; machine writing; man-machine integration

Abstract

With the development of economy and smart mobile communication equipment, the market has higher and higher requirements for timeliness and efficiency of news production. Manual news writing is gradually unable to meet the requirements of news production due to low efficiency, high cost and poor timeliness. The machine production technology of news gradually appeared. Primary machine writing completes sentence creation by defining grammar, understanding vocabulary semantics, and word order. However, as the length of the article increases, the accuracy is poor and the sentence ambiguity problem cannot be solved. Natural language processing and other technologies, through the construction of deep learning models, extracting text features, and using massive text content for training, have significantly improved the accuracy of the language, as evidenced by the emergence of machine writing software such as Xiaomingbot and Dreamwriter. However, current language processing models still have problems such as inability to understand abstract expressions, inability to generate creative words and sentences, and rigid language expressions. The integration of media workers and artificial intelligence can truly improve the accuracy, efficiency and artistry of language understanding, analysis, expression, and generation, thereby increasing the efficiency of news production and reducing the cost of news production.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article under the CC BY-NC license.

Download article (PDF)

TY  - CONF
AU  - Sheng Wang
PY  - 2022
DA  - 2022/02/01
TI  - Research on News Production Strategy Under Algorithm
BT  - Proceedings of the 2021 International Conference on Education, Language and Art (ICELA 2021)
PB  - Atlantis Press
SP  - 777
EP  - 781
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220131.141
DO  - 10.2991/assehr.k.220131.141
ID  - Wang2022
ER  -