@inproceedings{MieskesMuellerStrube2022,
  author    = {Margot Mieskes and Christoph M{\"u}ller and Michael Strube},
  title     = {Improving extractive dialogue summarization by utilizing human feedback},
  series = {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},
  editor    = {Vladan Devedzic},
  publisher = {ACTA Press},
  address   = {Anaheim [u.a.]},
  isbn      = {978-0-88986-629-4},
  url       = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-114120},
  pages     = {627 -- 632},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}