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    <abstract>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 ROUGE-based baselines. Also, the proposed method outperformed the baselines in recall. We im-plemented the proposed method in a GUI tool“Segment Matcher” that aids teachers to estab-lish a link between corresponding IUs acrossthe summary and source text.</abstract>
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%0 Conference Proceedings
%T Supporting content evaluation of student summaries by Idea Unit embedding
%A Gecchele, Marcello
%A Yamada, Hiroaki
%A Tokunaga, Takenobu
%A Sawaki, Yasuyo
%Y Yannakoudakis, Helen
%Y Kochmar, Ekaterina
%Y Leacock, Claudia
%Y Madnani, Nitin
%Y Pilán, Ildikó
%Y Zesch, Torsten
%S Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F gecchele-etal-2019-supporting
%X 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 ROUGE-based baselines. Also, the proposed method outperformed the baselines in recall. We im-plemented the proposed method in a GUI tool“Segment Matcher” that aids teachers to estab-lish a link between corresponding IUs acrossthe summary and source text.
%R 10.18653/v1/W19-4436
%U https://aclanthology.org/W19-4436
%U https://doi.org/10.18653/v1/W19-4436
%P 343-348

Markdown (Informal)

[Supporting content evaluation of student summaries by Idea Unit embedding](https://aclanthology.org/W19-4436) (Gecchele et al., BEA 2019)

• Supporting content evaluation of student summaries by Idea Unit embedding (Gecchele et al., BEA 2019)

ACL

• Marcello Gecchele, Hiroaki Yamada, Takenobu Tokunaga, and Yasuyo Sawaki. 2019. Supporting content evaluation of student summaries by Idea Unit embedding. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 343–348, Florence, Italy. Association for Computational Linguistics.