TY - CHAP U1 - Konferenzveröffentlichung A1 - Mieskes, Margot A1 - Müller, Christoph A1 - Strube, Michael ED - Devedzic, Vladan T1 - Improving extractive dialogue summarization by utilizing human feedback T2 - Proceedings of the IASTEAD international conference on artificial intelligence and applications as part of the 25th IASTED international multi-conference on applied informatics. February 12 - 14, 2007, Innsbruck, Austria N2 - Automatic summarization systems usually are trained and evaluated in a particular domain with fixed data sets. When such a system is to be applied to slightly different input, labor- and cost-intensive annotations have to be created to retrain the system. We deal with this problem by providing users with a GUI which allows them to correct automatically produced imperfect summaries. The corrected summary in turn is added to the pool of training data. The performance of the system is expected to improve as it adapts to the new domain. KW - multi-party dialogues KW - automatic summarization KW - GUI KW - feedback KW - learning KW - Zusammenfassung KW - Dialog KW - Annotation KW - Graphische Benutzeroberfläche KW - Maschinelles Lernen KW - Computerlinguistik KW - Digital Humanities Y1 - 2007 U6 - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-114120 UN - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-114120 SN - 978-0-88986-629-4 SB - 978-0-88986-629-4 SP - 627 EP - 632 PB - ACTA Press CY - Anaheim [u.a.] ER -