  Linking facts across documents is a challenging task, as the language used to
express the same information in a sentence can vary significantly, which
complicates the task of multi-document summarization. Consequently, existing
approaches heavily rely on hand-crafted features, which are domain-dependent
and hard to craft, or additional annotated data, which is costly to gather. To
overcome these limitations, we present a novel method, which makes use of two
types of sentence embeddings: universal embeddings, which are trained on a
large unrelated corpus, and domain-specific embeddings, which are learned
during training.
  To this end, we develop SemSentSum, a fully data-driven model able to
leverage both types of sentence embeddings by building a sentence semantic
relation graph. SemSentSum achieves competitive results on two types of
summary, consisting of 665 bytes and 100 words. Unlike other state-of-the-art
models, neither hand-crafted features nor additional annotated data are
necessary, and the method is easily adaptable for other tasks. To our
knowledge, we are the first to use multiple sentence embeddings for the task of
multi-document summarization.
