AI-Enhanced Natural Language Generation in Journalism

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Sanddep Raj

Abstract

This paper explores the integration of Artificial Intelligence (AI) technologies into the field of journalism, focusing on the application of AI-Enhanced Natural Language Generation (NLG) systems. The rapid advancements in NLG have opened new possibilities for automated content creation, making it a subject of growing interest in journalism. We delve into the various facets of NLG, including text generation models, content summarization, and sentiment analysis, and their implications for the journalism domain.

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How to Cite
AI-Enhanced Natural Language Generation in Journalism. (2016). Research-Gate Journal, 2(2). https://research-gate.in/index.php/Rgj/article/view/3
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Articles

How to Cite

AI-Enhanced Natural Language Generation in Journalism. (2016). Research-Gate Journal, 2(2). https://research-gate.in/index.php/Rgj/article/view/3

References

Brown, P. F., & Lai, J. C. (1994). A statistical approach to machine translation. Computational Linguistics, 20(3), 313-331.

Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159-165.

Dale, R., & Reiter, E. (1995). Computational interpretations of the Gricean maxims in the generation of referring expressions. Cognitive Science, 19(2), 233-263.

Barzilay, R., & McKeown, K. R. (2005). Sentence fusion for multidocument news summarization. Computational Linguistics, 31(3), 297-328.

Ribeiro, F. N., de Matos, D. M., & Santos, C. (2012). An empirical comparison of supervised learning algorithms on the task of Natural Language Generation. In Proceedings of the 2012 International Conference on Computational Processing of the Portuguese Language (PROPOR 2012), 121-131.