Wednesday, October 21
11:55 AM-12:15 PM
HST
Alii III

Automatic Summary Grading on Narrative Article for English Learner

Brief Paper ID: 47661
  1. Jia-Ling Koh
    National Taiwan Normal University
  2. aaa
    Han Wang
    National Taiwan Normal University
  3. Chen-Yu Huang
    Taipei Municipal Shilin High School of Commerce
  4. aaa
    Greg Lee
    National Taiwan Normal University

Abstract: The open questions about article's summarization can evaluate whether students understand the content of an article. However, it is a time-consuming task for teachers to give feedback and score. For solving this problem, we design a system to automatically grade the summarization questions on English narrative article without correct answer given by teachers. Accordingly, the students have more opportunities to practice with acquiring evaluation feedback in short time. In the proposed system, the article and the student's summary are represented by semantic graphs, which are compared to extract the matching features. Furthermore, the machine learning method is used to establish the grading classification model for the given summary. The experiment results show that the proposed method can achieve high overall precision when the articles have distinguishable words to express its focus.

Presider: Alia Sheety, Cabrini College

Topics

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