Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm
By Muthu Kumar Chandrasekaran, Guy Feigenblat, Eduard H. Hovy, Abhilasha Ravichander, Michal Shmueli-Scheuer, Anita de Waard. 2020
46 citations recorded by the source corpus.
We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.
Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks
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In Artificial Intelligence
- Jan Leike
- Jeff Wu
- Nazli Goharian
- Arman Cohan
- Ryan J. Lowe
- Daniel M. Ziegler
- Paul Christiano
- Sajad Sotudeh
In Natural Language Processing
- Nazli Goharian
- Arman Cohan
- Sajad Sotudeh
- Paul Christiano
- Daniel M. Ziegler
- Ryan J. Lowe
- Nissan Stiennon
- Jan Leike
In Evaluation & Benchmarks
Citations in the corpus
- Recursively Summarizing Books with Human Feedback (2021, background): “…2019; Cohan et al., 2018; Xiao and Carenini, 2019; Zhao et al., 2020; Sotudeh et al., 2020), and patents (Sharma et al., 2019), as well as multi-document summarization (Liu et al., 2018; Ma et al., 2020; Gharebagh et al., 2020; Chandrasekaran et al., 2020; Liu and Lapata, 2019a; Gao et al., 2020).”
- On Generating Extended Summaries of Long Documents (2020, methodology): “Longsumm The Longsumm dataset was provided for the Longsumm challenge (Chandrasekaran et al. 2020) whose aim was to generate extended summaries for scientific papers.”