Category-Driven Content Selection

نویسندگان

  • Rania Mohammed
  • Laura Perez-Beltrachini
  • Claire Gardent
چکیده

In this paper, we introduce a content selection method where the communicative goal is to describe entities of different categories (e.g., astronauts, universities or monuments). We argue that this method provides an interesting basis both for generating descriptions of entities and for semi-automatically constructing a benchmark on which to train, test and compare data-to-text generation systems.

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تاریخ انتشار 2016