Proceedings of ISP RAS

Narrabat — a Prototype Service for Stylish News Retelling

I.I. Dolgaleva (HSE, Moscow, Russia)
I.A. Gorshkov (HSE, Moscow, Russia)
R.E. Yavorskiy (HSE, Moscow, Russia)


Nowadays, news portals are forced to seek new methods of engaging the audience due to the increasing competition in today’s mass media. The growth in the loyalty of news service consumers may further a rise of popularity and, as a result, additional advertising revenue. Therefore, we propose the tool that is intended for stylish presenting of facts from a news feed. Its outputs are little poems that contain key facts from different news sources, based on the texts of Russian classics. The main idea of our algorithm is to use a collection of classical literature or poetry as a dictionary of style. The facts are extracted from news texts through Tomita Parser and then presented in the form similar to a sample from the collection. During our work, we tested several approaches for text generating, such as machine learning (including neural networks) and template-base method. The last method gave us the best performance, while the texts generated by the neural network are still needed to be improved. In this article, we present the current state of Narrabat, a prototype system rephrasing news we are currently working on, give examples of generated poems, and discuss some ideas for future performance improvement.


natural language processing; information extraction; natural language generation; tomita parser; neural networks


Proceedings of the Institute for System Programming, vol. 29, issue 4, 2017, pp. 325-336.

ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).

DOI: 10.15514/ISPRAS-2017-29(4)-23

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