Data-Driven News Generation for Automated Journalism
Leo Leppänen | Myriam Munezero | Mark Granroth-Wilding | Hannu Toivonen
Proceedings of the 10th International Conference on Natural Language Generation
Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce. In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability. Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent. We illustrate its practical application in the production of news articles about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design. We then draw some lessons for future automated journalism.
Towards automatic detection of antisocial behavior from texts
Myriam Munezero | Tuomo Kakkonen | Calkin Montero
Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011)