Improving extractive dialogue summarization by utilizing human feedback

被引：0
|
作者：
Mieskes, Margot [1 ]
Mueller, Christoph [1 ]
Strube, Michael [1 ]
机构：
[1] EML Res gGmbH, Schloss Wolfsbrunnenweg 33, D-69118 Heidelberg, Germany
来源：
PROCEEDINGS OF THE IASTED INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND APPLICATIONS | 2007年
关键词：
multi-party dialogues; automatic summarization; GUI; feedback; learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：627 / +
页数：3
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