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Using a keyness metric for single and multi document summarisation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Publication date08/2013
Host publicationProceedings of the MultiLing 2013 Workshop on Multilingual Multi-document summarization
Place of PublicationSofia, Bulgaria
PublisherAssociation for Computational Linguistics
Number of pages8
<mark>Original language</mark>English


In this paper we show the results of our participation in the MultiLing 2013 summarisation tasks. We participated with single-document and multi-document corpus-based summarisers for both Arabic and English languages. The summarisers used word frequency lists and log likelihood calculations to generate single
and multi document summaries. The single and multi summaries generated by our systems were evaluated by Arabic and English native speaker participants and by different automatic evaluation metrics, ROUGE, AutoSummENG, MeMoG and NPowER. We compare our results to other systems that participated in the same tracks on both Arabic and English languages.
Our single-document summarisers performed particularly well in the automatic evaluation with our English singledocument
summariser performing better on average than the results of the other participants. Our Arabic multi-document summariser performed well in the human evaluation ranking second.