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Start-ups from the machine learning sector sought to provide a solution for building a system for automatically generating news (natural language generation)

Country of Origin: Germany
Reference Number: TRDE20200727001
Publication Date: 27 July 2020

Summary

A large German publishing company is looking for a natural language generation solution - a system for automatically generating news in different domains out of structured data. As this request is part of an open innovation challenge targeted towards start-up companies, only start-ups with relevant machine learning solutions are sought for technical cooperation. The challenge closes on 15 August 2020.

Description

The German company with more than 3,000 employees is now in its fourth generation. It publishes daily newspapers as well as market-leading advertising journals. It also operates printing plants, mail services and trade journals.

The company is now focusing on their digital business. The work of journalists has significantly changed over the last years as a result of the dawn of digitalization. The technological progress, the emergence of new narrative forms, the efficiency increment of automated workflows, and the availability of big data, has created a new form of journalism known as robot-journalism. Within this field the term NLG (natural language generation) refers to explicitly programmed systems, for example, to write news from structured data. Such systems are characterized by the fact that they do not need any human intervention after they have been programmed. However, the development of such a system is linked to deep know-how in different fields such as machine learning and cloud computing.

The German company is looking for start-ups with know-how in machine learning and NLG for a technical cooperation that will help them develop this kind of system to support their newsrooms in automatically writing news based on data coming from fields like sports or finance.

IMPORTANT: This technology request refers to an innovation challenge published on an open internet platform that was issued by a major city in North Rhine-Westphalia. If a start-up expresses an interest before the closing date of 15 August, it will be guided towards this open innovation platform. The start-up company takes part in the challenge with this application. If it is selected, it will be invited to spend five working days in workshops with the company on a solution concept as well as in training workshops on the German market and on market entry to the regional ecosystem.

All submissions will get feedback from the German company on this open platform. The challenge closes on August 15, 2020. Mind that posts on this platform are not confidential. Beside open discussions on the platform, sharing of confidential information will be made possible on demand. After that, the firm will select the start-ups with whom they would like to cooperate in the development of a solution.

Expertise sought

To develop a mature natural language generation (NLG) system that can be successfully brought into production, following requirements need to be fulfilled:

– Transparency: Understanding of how the text are generated
– Accuracy: No misleading facts
– Modifiability and Transferability: Transferability to other domains
– Natural flow of speech: The generated texts should be written as naturally as possible

The system must be developed in the programming language Python. Also, the underlying algorithms must be based on controllable end-to-end models such as gpt2. Template-based systems must not be used.

Requested partner

The German publishing company seeks start-up companies from the machine learning sector for technical cooperation agreements.

The ideal partners would be young companies that have developed promising approaches and products for setting up language generation solutions to support the company's newsrooms in automatically writing news based on data coming from fields like sport or finance.

Together with the German partner they can discuss concepts, products and ideas for the challenge and develop a system that can be successfully brought into production.

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