An exploratory study into automated précis grading

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 précis texts. A précis is a written text that provides a coherent summary of main points of a spoken or written text. We present a corpus of English précis 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 précis texts with only a moderate error margin

Session 4. Training

Subtitling short films to improve writing and translation skills / Noa Talaván (Universidad Nacional de Educación a Distancia), Pilar Rodríguez-Arancón (Universidad Nacional de Educación a Distancia) ; Exploring audiovisual translation in vocational education and training: free commentary in teacher training / Jennifer Lertola (Università del Piemonte Orientale) ; The relation between subtitle reading, cognitive load and comprehension in Emi lecture / Senne M. Van Hoecke (University of Antwerp), Iris Schrijver (University of Antwerp), Isabelle R. Robert (University of Antwerp) ; Accessible filmmaker: towards the definition of a professional profile / Florencia Fascioli Álvarez (Universidade de Vigo & Universidad Católica del Uruguay). Chair: Juan Pedro Rica (Universidad Complutense de Madrid

An exploratory study into automated pr\ue9cis grading

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 précis texts. A précis is a written text that provides a coherent summary of main points of a spoken or written text. We present a corpus of English précis 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 précis texts with only a moderate error margin