  This paper presents a new approach of automatic text summarization which
combines domain oriented text analysis (DoTA) and rhetorical structure theory
(RST) in a grammar form: the attributed rhetorical structure grammar (ARSG),
where the non-terminal symbols are domain keywords, called domain relations,
while the rhetorical relations serve as attributes. We developed machine
learning algorithms for learning such a grammar from a corpus of sample domain
texts, as well as parsing algorithms for the learned grammar, together with
adjustable text summarization algorithms for generating domain specific
summaries. Our practical experiments have shown that with support of domain
knowledge the drawback of missing very large training data set can be
effectively compensated. We have also shown that the knowledge based approach
may be made more powerful by introducing grammar parsing and RST as inference
engine. For checking the feasibility of model transfer, we introduced a
technique for mapping a grammar from one domain to others with acceptable cost.
We have also made a comprehensive comparison of our approach with some others.
