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        <title>Multi-Document Summarization Using Multiple-Sequence Alignment</title>
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        <namePart type="given">Finley</namePart>
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            <title>Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)</title>
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            <namePart type="given">Maria</namePart>
            <namePart type="given">Teresa</namePart>
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    <abstract>This paper describes a novel clustering-based text summarization system that uses Multiple Sequence Alignment to improve the alignment of sentences within topic clusters. While most current clustering-based summarization systems base their summaries only on the common information contained in a collection of highly-related sentences, our system constructs more informative summaries that incorporate both the redundant and unique contributions of the sentences in the cluster. When evaluated using ROUGE, the summaries produced by our system represent a substantial improvement over the baseline, which is at 63% of the human performance.</abstract>
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%0 Conference Proceedings
%T Multi-Document Summarization Using Multiple-Sequence Alignment
%A Lacatusu, V. Finley
%A Maiorano, Steven J.
%A Harabagiu, Sanda M.
%Y Lino, Maria Teresa
%Y Xavier, Maria Francisca
%Y Ferreira, Fátima
%Y Costa, Rute
%Y Silva, Raquel
%S Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)
%D 2004
%8 May
%I European Language Resources Association (ELRA)
%C Lisbon, Portugal
%F lacatusu-etal-2004-multi
%X This paper describes a novel clustering-based text summarization system that uses Multiple Sequence Alignment to improve the alignment of sentences within topic clusters. While most current clustering-based summarization systems base their summaries only on the common information contained in a collection of highly-related sentences, our system constructs more informative summaries that incorporate both the redundant and unique contributions of the sentences in the cluster. When evaluated using ROUGE, the summaries produced by our system represent a substantial improvement over the baseline, which is at 63% of the human performance.
%U http://www.lrec-conf.org/proceedings/lrec2004/pdf/408.pdf

Markdown (Informal)

[Multi-Document Summarization Using Multiple-Sequence Alignment](http://www.lrec-conf.org/proceedings/lrec2004/pdf/408.pdf) (Lacatusu et al., LREC 2004)

• Multi-Document Summarization Using Multiple-Sequence Alignment (Lacatusu et al., LREC 2004)

ACL

• V. Finley Lacatusu, Steven J. Maiorano, and Sanda M. Harabagiu. 2004. Multi-Document Summarization Using Multiple-Sequence Alignment. In Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), Lisbon, Portugal. European Language Resources Association (ELRA).