Summary of Reflectsumm: a Benchmark For Course Reflection Summarization, by Yang Zhong et al.
ReflectSumm: A Benchmark for Course Reflection Summarization
by Yang Zhong, Mohamed Elaraby, Diane Litman, Ahmed Ashraf Butt, Muhsin Menekse
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces ReflectSumm, a novel dataset designed to facilitate developing and evaluating summarization techniques for students’ reflective writing. This dataset is specifically tailored to real-world scenarios with limited training data, making it a valuable tool for the opinion summarization domain and education sector. The dataset includes diverse summarization tasks and comprehensive metadata, enabling researchers to explore various research questions and support different applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ReflectSumm is a new dataset designed to help develop better ways to summarize students’ written reflections. It’s meant to work in real-world situations with limited training data, which makes it useful for the education sector and beyond. The dataset includes many different summarization tasks and lots of extra information that helps researchers explore different ideas. |
Keywords
» Artificial intelligence » Summarization
