  We introduce a novel graph-based framework for abstractive meeting speech
summarization that is fully unsupervised and does not rely on any annotations.
Our work combines the strengths of multiple recent approaches while addressing
their weaknesses. Moreover, we leverage recent advances in word embeddings and
graph degeneracy applied to NLP to take exterior semantic knowledge into
account, and to design custom diversity and informativeness measures.
Experiments on the AMI and ICSI corpus show that our system improves on the
state-of-the-art. Code and data are publicly available, and our system can be
interactively tested.
