ReflectSumm: A Benchmark for Course Reflection Summarization
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913324599345152 |
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| author | Zhong, Yang Elaraby, Mohamed Litman, Diane Butt, Ahmed Ashraf Menekse, Muhsin |
| author_facet | Zhong, Yang Elaraby, Mohamed Litman, Diane Butt, Ahmed Ashraf Menekse, Muhsin |
| contents | 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, %practical tasks 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_19012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ReflectSumm: A Benchmark for Course Reflection Summarization Zhong, Yang Elaraby, Mohamed Litman, Diane Butt, Ahmed Ashraf Menekse, Muhsin Computation and Language Artificial Intelligence 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, %practical tasks 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. |
| title | ReflectSumm: A Benchmark for Course Reflection Summarization |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.19012 |