@inproceedings{elaraby-etal-2024-reflectsumm-benchmark,
title = "{R}eflect{S}umm: A Benchmark for Course Reflection Summarization",
author = "Elaraby, Mohamed and
Zhong, Yang and
Litman, Diane and
Butt, Ahmed Ashraf and
Menekse, Muhsin",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1207",
pages = "13819--13846",
abstract = "This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students{'} reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, with potential implications in the opinion summarization domain in general and the educational domain in particular. The dataset encompasses a diverse range of summarization tasks and includes comprehensive metadata, enabling the exploration of various research questions and supporting different applications. To showcase its utility, we conducted extensive evaluations using multiple state-of-the-art baselines. The results provide benchmarks for facilitating further research in this area.",
}
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<abstract>This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students’ reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, with potential implications in the opinion summarization domain in general and the educational domain in particular. The dataset encompasses a diverse range of summarization tasks and includes comprehensive metadata, enabling the exploration of various research questions and supporting different applications. To showcase its utility, we conducted extensive evaluations using multiple state-of-the-art baselines. The results provide benchmarks for facilitating further research in this area.</abstract>
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%0 Conference Proceedings
%T ReflectSumm: A Benchmark for Course Reflection Summarization
%A Elaraby, Mohamed
%A Zhong, Yang
%A Litman, Diane
%A Butt, Ahmed Ashraf
%A Menekse, Muhsin
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F elaraby-etal-2024-reflectsumm-benchmark
%X This paper introduces ReflectSumm, a novel summarization dataset specifically designed for summarizing students’ reflective writing. The goal of ReflectSumm is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, with potential implications in the opinion summarization domain in general and the educational domain in particular. The dataset encompasses a diverse range of summarization tasks and includes comprehensive metadata, enabling the exploration of various research questions and supporting different applications. To showcase its utility, we conducted extensive evaluations using multiple state-of-the-art baselines. The results provide benchmarks for facilitating further research in this area.
%U https://aclanthology.org/2024.lrec-main.1207
%P 13819-13846
Markdown (Informal)
[ReflectSumm: A Benchmark for Course Reflection Summarization](https://aclanthology.org/2024.lrec-main.1207) (Elaraby et al., LREC-COLING 2024)
ACL
- Mohamed Elaraby, Yang Zhong, Diane Litman, Ahmed Ashraf Butt, and Muhsin Menekse. 2024. ReflectSumm: A Benchmark for Course Reflection Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 13819–13846, Torino, Italia. ELRA and ICCL.