Finding middle ground? Multi-objective Natural Language Generation from time-series data

Abstract

A Natural Language Generation (NLG) system is able to generate text from non-linguistic data, ideally personalising the content to a user's specific needs. In some cases, however, there are multiple stakeholders with their own individual goals, needs and preferences. In this paper, we explore the feasibility of combining the preferences of two different user groups, lecturers and students, when generating summaries in the context of student feedback generation. The preferences of each user group are modelled as a multivariate optimisation function, therefore the task of generation is seen as a multi-objective (MO) optimisation task, where the two functions are combined into one. This initial study shows that treating the preferences of each user group equally smooths the weights of the MO function, in a way that preferred content of the user groups is not presented in the generated summary.