This paper discusses the computer-assisted content evaluation of summaries. We propose a method to make a correspondence between the segments of the source text and its summary. As a unit of the segment, we adopt “Idea Unit (IU)” which is proposed in Applied Linguistics. Introducing IUs enables us to make a correspondence even for the sentences that contain multiple ideas. The IU correspondence is made based on the similarity between vector representations of IU. An evaluation experiment with two source texts and 20 summaries showed that the proposed method is more robust against rephrased expressions than the conventional ROUGEbased baselines. Also, the proposed method outperformed the baselines in recall. We implemented the proposed method in a GUI tool “Segment Matcher” that aids teachers to establish a link between corresponding IUs across the summary and source text.
Marcello Gecchele, Hiroaki Yamada, Takenobu Tokunaga and Yasuyo Sawaki. 2019. Supporting content evaluation of student summaries by Idea Unit embedding. In Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications (BEA@ACL2019).