Using Mechanical Turk to Create a Corpus of Arabic Summaries
By Mahmoud El-Haj, Udo Kruschwitz, Chris Fox. 2010
109 citations recorded by the source corpus.
Subfields: Natural Language Processing, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Thomas Scialom
- Paul-Alexis Dray
- Jacopo Staiano
- Sylvain Lamprier
- Benjamin Piwowarski
- Rim Koulali
- Leon Derczynski
- Kalina Bontcheva
In Evaluation & Benchmarks
- Leon Derczynski
- Rim Koulali
- Paul-Alexis Dray
- Benjamin Piwowarski
- Kalina Bontcheva
- Jacopo Staiano
- Sylvain Lamprier
- Mahmoud El-Haj
Citations in the corpus
- MLSUM: The Multilingual Summarization Corpus (2020, background): “For Arabic, there exist the Essex Arabic Summaries Corpus (EASC) (El-Haj et al., 2010) and KALIMAT (El-Haj and Koulali, 2013); those comprise circa 1k and 20k samples, respectively.”
- Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines (2014, background): “This feature is particularly useful for those that work on less-resourced languages such as Arabic (El-Haj et al., 2010) and Urdu (Zaidan and CallisonBurch, 2011). Irvine and Klementiev (2010) demonstrated”
- KALIMAT a multipurpose Arabic corpus (2013, methodology): “Gen-Summ (El-Haj et al. 2010) is a single document summariser based on the VSM model (Salton et al. 1975) that takes an Arabic document and its first sentence and returns an extractive summary.”