  Automatic text summarization (ATS) has recently achieved impressive
performance thanks to recent advances in deep learning and the availability of
large-scale corpora. To make the summarization results more faithful, this
paper presents an unsupervised approach that combines rhetorical structure
theory, deep neural model and domain knowledge concern for ATS. This
architecture mainly contains three components: domain knowledge base
construction based on representation learning, attentional encoder-decoder
model for rhetorical parsing and subroutine-based model for text summarization.
Domain knowledge can be effectively used for unsupervised rhetorical parsing
thus rhetorical structure trees for each document can be derived. In the
unsupervised rhetorical parsing module, the idea of translation was adopted to
alleviate the problem of data scarcity. The subroutine-based summarization
model purely depends on the derived rhetorical structure trees and can generate
content-balanced results. To evaluate the summary results without golden
standard, we proposed an unsupervised evaluation metric, whose hyper-parameters
were tuned by supervised learning. Experimental results show that, on a
large-scale Chinese dataset, our proposed approach can obtain comparable
performances compared with existing methods.
