@inproceedings{481cbe1c856f4f75aac325f416c0d7a5,
title = "Automated summarization evaluation (ASE) using natural language processing tools",
abstract = "Summarization is an effective strategy to promote and enhance learning and deep comprehension of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation of students{\textquoteright} summaries requires time and effort. This problem has led to the development of automated models of summarization quality. However, these models often rely on features derived from expert ratings of student summarizations of specific source texts and are therefore not generalizable to summarizations of new texts. Further, many of the models rely of proprietary tools that are not freely or publicly available, rendering replications difficult. In this study, we introduce an automated summarization evaluation (ASE) model that depends strictly on features of the source text or the summary, allowing for a purely text-based model of quality. This model effectively classifies summaries as either low or high quality with an accuracy above 80%. Importantly, the model was developed on a large number of source texts allowing for generalizability across texts. Further, the features used in this study are freely and publicly available affording replication.",
keywords = "Discourse, Machine learning, Natural language processing, Summarization, Summary scoring, Writing",
author = "Crossley, {Scott A.} and Minkyung Kim and Laura Allen and Danielle McNamara",
note = "Funding Information: Acknowledgments. This research was supported in part by the Institute for Education Sciences (IES R305A180261). Ideas expressed in this material are those of the authors and do not necessarily reflect the views of the IES. We would also like to express thanks to Amy Johnson, Kristopher Kopp, and Cecile Perret for their help in collecting the data. Funding Information: This research was supported in part by the Institute for Education Sciences (IES R305A180261). Ideas expressed in this material are those of the authors and do not necessarily reflect the views of the IES. We would also like to express thanks to Amy Johnson, Kristopher Kopp, and Cecile Perret for their help in collecting the data. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 20th International Conference on Artificial Intelligence in Education, AIED 2019 ; Conference date: 25-06-2019 Through 29-06-2019",
year = "2019",
doi = "10.1007/978-3-030-23204-7_8",
language = "English (US)",
isbn = "9783030232030",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "84--95",
editor = "Seiji Isotani and Eva Mill{\'a}n and Amy Ogan and Bruce McLaren and Peter Hastings and Rose Luckin",
booktitle = "Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings",
}