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Article published In: Linguistic Recycling: The process of quoting in increasingly mediatized settings
Edited by Lauri Haapanen and Daniel Perrin
[AILA Review 33] 2020
► pp. 67–85

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Recycling a genre for news automation

The production of Valtteri the Election Bot

 | University of Jyväskylä
 | University of Helsinki
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Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 7 October 2020
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https://doi.org/10.1075/aila.00030.haa
Abstract
The amount of available digital data is increasing at a tremendous rate. These data, however, are of limited use unless converted into a user-friendly form. We took on this task and built a natural language generation (NLG) driven system that generates journalistic news stories about elections without human intervention. In this paper, after presenting an overview of state-of-the-art technologies in NLG, we explain systematically how we identified and then recontextualized the determinant aspects of the genre of an online news story in the algorithm of our NLG software. In the discussion, we introduce the key results of a user test we carried out and some improvements that these results suggest. Then, after relating the news items that our NLG system generates to general aspects of genres and their evolution, we conclude by questioning the idea that journalistic NLG systems should mimic journalism written by humans. Instead, we suggest that developmental work in the field of news automation should aim to create a new genre based on the inherent strengths of NLG. Finally, we present a few suggestions as to what this genre could include.
Article outline
  • 1.Introduction: Converting large data sets into user-friendly form
  • 2.State-of-the-art in NLG: A spectrum from ruled-based to training-driven methods
  • 3.Case study: The production of Valtteri the Election Bot
    • 3.1Domain selection: Topicality and relevance
    • 3.2Document planning: Readers’ preferences, news values, and rhetorical structures
    • 3.3Microplanning: Lexicalization and aggregation
    • 3.4Realization: Orthography and typography
  • 4.Discussion: Toward a distinctive genre of online news story
    • 4.1Challenge: Trade-offs to improve Valtteri’s performance
    • 4.2Solution: From non-typicality to a new genre
  • 5.Conclusion
  • Notes
  • References
References (34)
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2025. Proceedings of the 2025 ACM Designing Interactive Systems Conference, ► pp. 2975 ff. DOI logo
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2022. Adapting Media Self-Regulation to the Era of News Automation. In Futures of Journalism, ► pp. 81 ff. DOI logo

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