MUSEEC: A Multilingual Text Summarization Tool

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.