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Summarization Evaluation meets Short-Answer Grading

Margot Mieskes
Hochschule Darmstadt, Germany

Ulrike Padó
Hochschule für Technik Stuttgart, Germany

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Ingår i: Proceedings of the 8th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2019), September 30, Turku Finland

Linköping Electronic Conference Proceedings 164:8, s. 79-85

NEALT Proceedings Series 39:8, s. 79-85

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Publicerad: 2019-09-30

ISBN: 978-91-7929-998-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

Summarization Evaluation and Short-Answer Grading share the challenge of automatically evaluating content quality. Therefore, we explore the use of ROUGE, a well-known Summarization Evaluation method, for Short-Answer Grading. We find a reliable ROUGE parametrization that is robust across corpora and languages and produces scores that are significantly correlated with human short-answer grades. ROUGE adds no information to Short-Answer Grading NLP-based machine learning features in a by-corpus evaluation. However, on a question-by-question basis, we find that the ROUGE Recall score may outperform standard NLP features. We therefore suggest to use ROUGE within a framework for per-question feature selection or as a reliable and reproducible baseline for SAG.

Nyckelord

short-answer grading, summarization evaluation, ROUGE

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