TY - GEN
T1 - MUSEEC
T2 - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
AU - Litvak, Marina
AU - Vanetik, Natalia
AU - Last, Mark
AU - Churkin, Elena
N1 - Funding Information:
This work was partially funded by the U.S. Department of the Navy, Office of Naval Research.
Publisher Copyright:
© 2016 Association for Computational Linguistics.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - The MUSEEC (Multilingual SEntence Extraction and Compression) summarization tool implements several extractive summarization techniques - at the level of complete and compressed sentences - that can be applied, with some minor adaptations, to documents in multiple languages. The current version of MUSEEC provides the following summarization methods: (1) MUSE - a supervised summarizer, based on a genetic algorithm (GA), that ranks document sentences and extracts top-ranking sentences into a summary, (2) POLY - an unsupervised summarizer, based on linear programming (LP), that selects the best extract of document sentences, and (3) WECOM - an unsupervised extension of POLY that compiles a document summary from compressed sentences. In this paper, we provide an overview of MUSEEC methods and its architecture in general.
AB - The MUSEEC (Multilingual SEntence Extraction and Compression) summarization tool implements several extractive summarization techniques - at the level of complete and compressed sentences - that can be applied, with some minor adaptations, to documents in multiple languages. The current version of MUSEEC provides the following summarization methods: (1) MUSE - a supervised summarizer, based on a genetic algorithm (GA), that ranks document sentences and extracts top-ranking sentences into a summary, (2) POLY - an unsupervised summarizer, based on linear programming (LP), that selects the best extract of document sentences, and (3) WECOM - an unsupervised extension of POLY that compiles a document summary from compressed sentences. In this paper, we provide an overview of MUSEEC methods and its architecture in general.
UR - http://www.scopus.com/inward/record.url?scp=84985902807&partnerID=8YFLogxK
U2 - 10.18653/v1/p16-4013
DO - 10.18653/v1/p16-4013
M3 - Conference contribution
AN - SCOPUS:84985902807
T3 - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - System Demonstrations
SP - 73
EP - 78
BT - 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - System Demonstrations
PB - Association for Computational Linguistics (ACL)
Y2 - 7 August 2016 through 12 August 2016
ER -