@inproceedings{aed0789f703f4b9b91ff21543c96e064,
title = "A new approach to improving multilingual summarization using a genetic algorithm",
abstract = "Automated summarization methods can be defined as “language-independent,” if they are not based on any language-specific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a language-independent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm. We tested our methodology on two languages-English and Hebrew-and evaluated its performance with ROUGE-1 Recall vs. state-of-the-art extractive summarization approaches. Our results show that MUSE performs better than the best known multilingual approach (TextRank1) in both languages. Moreover, our experimental results on a bilingual (English and Hebrew) document collection suggest that MUSE does not need to be retrained on each language and the same model can be used across at least two different languages.",
author = "Marina Litvak and Mark Last and Menahem Friedman",
note = "Publisher Copyright: {\textcopyright} 2010 Association for Computational Linguistics.; 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 ; Conference date: 11-07-2010 Through 16-07-2010",
year = "2010",
month = jan,
day = "1",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "927--936",
editor = "Jan Hajic and Sandra Carberry and Stephen Clark",
booktitle = "ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Conference Proceedings",
address = "United States",
}