@inproceedings{de-clercq-van-hoecke-2020-exploratory,
    title = "An Exploratory Study into Automated Pr{\'e}cis Grading",
    author = "De Clercq, Orphee  and
      Van Hoecke, Senne",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/moar-dois/2020.lrec-1.50/",
    pages = "397--404",
    language = "eng",
    ISBN = "979-10-95546-34-4",
    abstract = "Automated writing evaluation is a popular research field, but the main focus has been on evaluating argumentative essays. In this paper, we consider a different genre, namely pr{\'e}cis texts. A pr{\'e}cis is a written text that provides a coherent summary of main points of a spoken or written text. We present a corpus of English pr{\'e}cis texts which all received a grade assigned by a highly-experienced English language teacher and were subsequently annotated following an exhaustive error typology. With this corpus we trained a machine learning model which relies on a number of linguistic, automatic summarization and AWE features. Our results reveal that this model is able to predict the grade of pr{\'e}cis texts with only a moderate error margin."
}