Improving extractive dialogue summarization by utilizing human feedback

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. KEY WORDS Multi-Party Dialogues, Automatic Summarization, GUI, Feedback, Learning

• Type: Conference
• Year: 2007
• Where: AIA
• Authors: Margot Mieskes, Christoph Müller, Michael Strube
• Keywords: AIA 2007, Artificial Intelligence, Automatic Summarization, Automatic Summarization Systems, WORDS Multi-Party Dialogues