@inproceedings{mille-etal-2020-case,
    title = "A Case Study of {NLG} from Multimedia Data Sources: Generating Architectural Landmark Descriptions",
    author = "Mille, Simon  and
      Symeonidis, Spyridon  and
      Rousi, Maria  and
      Marimon Felipe, Montserrat  and
      Stavrothanasopoulos, Klearchos  and
      Alvanitopoulos, Petros  and
      Carlini Salguero, Roberto  and
      Grivolla, Jens  and
      Meditskos, Georgios  and
      Vrochidis, Stefanos  and
      Wanner, Leo",
    editor = "Castro Ferreira, Thiago  and
      Gardent, Claire  and
      Ilinykh, Nikolai  and
      van der Lee, Chris  and
      Mille, Simon  and
      Moussallem, Diego  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)",
    month = "12",
    year = "2020",
    address = "Dublin, Ireland (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.webnlg-1.1",
    pages = "2--14",
    abstract = "In this paper, we present a pipeline system that generates architectural landmark descriptions using textual, visual and structured data. The pipeline comprises five main components:(i) a textual analysis component, which extracts information from Wikipedia pages; (ii)a visual analysis component, which extracts information from copyright-free images; (iii) a retrieval component, which gathers relevant (property, subject, object) triples from DBpedia; (iv) a fusion component, which stores the contents from the different modalities in a Knowledge Base (KB) and resolves the conflicts that stem from using different sources of information; (v) an NLG component, which verbalises the resulting contents of the KB. We show that thanks to the addition of other modalities, we can make the verbalisation of DBpedia triples more relevant and/or inspirational.",
}