caa a22 4500 463251504 CHVBK 20180405153341.0 cr unu---uuuuu 170326e20070601xx s 000 0 eng 10.1007/s10791-006-9017-1 doi (NATIONALLICENCE)springer-10.1007/s10791-006-9017-1 Learning-based summarisation of XML documents [Elektronische Daten] [Massih Amini, Anastasios Tombros, Nicolas Usunier, Mounia Lalmas] Documents formatted in eXtensible Markup Language (XML) are available in collections of various document types. In this paper, we present an approach for the summarisation of XML documents. The novelty of this approach lies in that it is based on features not only from the content of documents, but also from their logical structure. We follow a machine learning, sentence extraction-based summarisation technique. To find which features are more effective for producing summaries, this approach views sentence extraction as an ordering task. We evaluated our summarisation model using the INEX and SUMMAC datasets. The results demonstrate that the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that the learnable system is also effective and well-suited to the task of summarisation in the context of XML documents. Our approach is generic, and is therefore applicable, apart from entire documents, to elements of varying granularity within the XML tree. We view these results as a step towards the intelligent summarisation of XML documents. Springer Science+Business Media, LLC, 2006 Text summarisation nationallicence XML documents nationallicence Machine learning nationallicence Ranking functions nationallicence Amini Massih University Pierre and Marie Curie, 8, rue du capitaine Scott, 75015, Paris, France aut Tombros Anastasios Department of Computer Science, Queen Mary, University of London, E1 4NS, London, United Kingdom aut Usunier Nicolas University Pierre and Marie Curie, 8, rue du capitaine Scott, 75015, Paris, France aut Lalmas Mounia Department of Computer Science, Queen Mary, University of London, E1 4NS, London, United Kingdom aut Information Retrieval Kluwer Academic Publishers 10/3(2007-06-01), 233-255 1386-4564 10:3<233 2007 10 10791 https://doi.org/10.1007/s10791-006-9017-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10791-006-9017-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Amini Massih University Pierre and Marie Curie, 8, rue du capitaine Scott, 75015, Paris, France aut NATIONALLICENCE 700 1- Tombros Anastasios Department of Computer Science, Queen Mary, University of London, E1 4NS, London, United Kingdom aut NATIONALLICENCE 700 1- Usunier Nicolas University Pierre and Marie Curie, 8, rue du capitaine Scott, 75015, Paris, France aut NATIONALLICENCE 700 1- Lalmas Mounia Department of Computer Science, Queen Mary, University of London, E1 4NS, London, United Kingdom aut NATIONALLICENCE 773 0- Information Retrieval Kluwer Academic Publishers 10/3(2007-06-01), 233-255 1386-4564 10:3<233 2007 10 10791 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer