Improving extractive dialogue summarization by utilizing human feedback

Authors:
Margot Mieskes;Christoph Müller;Michael Strube
Affiliations:
EML Research gGmbH, Heidelberg, Germany;EML Research gGmbH, Heidelberg, Germany;EML Research gGmbH, Heidelberg, Germany
Venue:
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Year:
2007

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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.