  Multi-document summarization is a process of automatic generation of a
compressed version of the given collection of documents. Recently, the
graph-based models and ranking algorithms have been actively investigated by
the extractive document summarization community. While most work to date
focuses on homogeneous connecteness of sentences and heterogeneous connecteness
of documents and sentences (e.g. sentence similarity weighted by document
importance), in this paper we present a novel 3-layered graph model that
emphasizes not only sentence and document level relations but also the
influence of under sentence level relations (e.g. a part of sentence
similarity).
