Towards Summarization for Social Media - Results of the TL;DR Challenge
By Shahbaz Syed, Michael Völske, Nedim Lipka, Benno Stein, Hinrich Schütze, Martin Potthast. 2019
7 citations recorded by the source corpus.
In this paper, we report on the results of the TL;DR challenge, discussing an extensive manual evaluation of the expected properties of a good summary based on analyzing the comments provided by human annotators.
Subfields: Artificial Intelligence, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Elizabeth Clark
- Sebastian Gehrmann
- Thibault Sellam
- Yejin Choi
- Percy Liang
- Noah A. Smith
- Mohit Iyyer
- Jianfeng Gao
In Natural Language Processing
- Thibault Sellam
- Elizabeth Clark
- Sebastian Gehrmann
- Yejin Choi
- Yonatan Belinkov
- Tatsunori B. Hashimoto
- Greg Durrett
- Percy Liang
Citations in the corpus
- Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, background): “…has been a long history of shared tasks in the NLG community addressing a much more diverse set of tasks starting with referring expression generation (Gatt et al., 2008), but which have also covered tasks such as summarization (Syed et al., 2019) and data-to-text generation (Dusek et al., 2020).”