A New Approach to Improving Multilingual Summarization Using a Genetic Algorithm
By Marina Litvak, Mark Last, Menahem Friedman. 2010
117 citations recorded by the source corpus.
Subfields: 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 Natural Language Processing
- Xiaojun Wan
- Thomas Scialom
- Chengqing Zong
- Jiajun Zhang
- Paul-Alexis Dray
- Jacopo Staiano
- Sylvain Lamprier
- Benjamin Piwowarski
Citations in the corpus
- MLSUM: The Multilingual Summarization Corpus (2020, methodology): “Litvak et al. (2010) proposed to improve multilingual summarization using a ge-netic algorithm.”
- Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing (2016, background): “Pilot studies investigate this task by utilizing only one language side information (source or target language side) [1], [22]–[26].”
- Improving the Estimation of Word Importance for News Multi-Document Summarization (2014, background): “Most existing work directly focuses on predicting sentence importance, with emphasis on the formalization of the problem (Kupiec et al., 1995; Celikyilmaz and Hakkani-Tur, 2010; Litvak et al., 2010).”
- Phrase-based Compressive Cross-Language Summarization (2015, background): “Before Wan (2011)’s work that explicitly utilizes bilingual information in a graph-based framework, earlier methods often use information only from one language (de Chal-endar et al., 2005; Pingali et al., 2007; Orasan and Chiorean, 2008; Litvak et al., 2010).”
- System Combination for Multi-document Summarization (2015, background): “Though prior work has extensively investigated features that are indicative of important words (Yih et al., 2007; Hong and Nenkova, 2014) or sentences (Litvak et al., 2010; Ouyang et al., 2011), little work has focused on designing global features defined over the summary.”
- Improving the Estimation of Word Importance for News Multi-Document Summarization - Extended Technical Report (2016, background): “Most existing work directly focuses on predicting sentence importance, with emphasis on the formalization of the problem (Kupiec et al., 1995; Celikyilmaz and Hakkani-Tur, 2010; Litvak et al., 2010).”