  In this paper, we present RTSUM, an unsupervised summarization framework that
utilizes relation triples as the basic unit for summarization. Given an input
document, RTSUM first selects salient relation triples via multi-level salience
scoring and then generates a concise summary from the selected relation triples
by using a text-to-text language model. On the basis of RTSUM, we also develop
a web demo for an interpretable summarizing tool, providing fine-grained
interpretations with the output summary. With support for customization
options, our tool visualizes the salience for textual units at three distinct
levels: sentences, relation triples, and phrases. The codes,are publicly
available.
