MUSEEC: A Multilingual text summarization tool

Marina Litvak, Natalia Vanetik, Mark Last, Elena Churkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - System Demonstrations
PublisherAssociation for Computational Linguistics (ACL)
Pages73-78
Number of pages6
ISBN (Electronic)9781510827615
DOIs
StatePublished - 1 Jan 2016
Event54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016

Publication series

Name54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - System Demonstrations

Conference

Conference54th Annual Meeting of the Association for Computational Linguistics, ACL 2016
Country/TerritoryGermany
CityBerlin
Period7/08/1612/08/16

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