Improving extractive dialogue summarization by utilizing human feedback
Margot Mieskes, Christoph Müller, Michael Strube · 2007
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 automati-cally produced imperfect summaries. The corrected sum-mary in turn is added to the pool of training data. The per-formance of the system is expected to improve as it adapts to the new domain.