講義再現システムにおけるスライド重要度抽出

概要

論文の詳細を見る
This paper describes a method for extracting importance of slides in a lecture review system. We introduce "index of importance" to quantitatively evaluate importance of slides. The index of importance is subjective evaluation value that is attached to each slide by lecturers. Firstly, the lecture review system extracts the index of importance of the slide by using a multi-layer neural network (MLN). In a MLN learning process, eight types of nonlinguistic informations, such as the presentation time of the slide, are used as inputs and the index of importance given by lecturers are set as outputs. Secondly, the index of importance is modified by using the other MLN which has two types of inputs; one is the index of importance and the other is similarities between the slide and adjacent slides. The similarities are calculated with key-word vectors extracted by linguistic informations in slides. The experimental results showed that the index of importance extracted by the system is highly correlated with the index attached by lecturers. As a result, the lecture review system with the proposed extraction method can properly detect key slides and helps students to learn the contents of a lecture effectively.
社団法人　人工知能学会の論文
2002-11-01