  Graph-based extractive document summarization relies on the quality of the
sentence similarity graph. Bag-of-words or tf-idf based sentence similarity
uses exact word matching, but fails to measure the semantic similarity between
individual words or to consider the semantic structure of sentences. In order
to improve the similarity measure between sentences, we employ off-the-shelf
deep embedding features and tf-idf features, and introduce a new text
similarity metric. An improved sentence similarity graph is built and used in a
submodular objective function for extractive summarization, which consists of a
weighted coverage term and a diversity term. A Transformer based compression
model is developed for sentence compression to aid in document summarization.
Our summarization approach is extractive and unsupervised. Experiments
demonstrate that our approach can outperform the tf-idf based approach and
achieve state-of-the-art performance on the DUC04 dataset, and comparable
performance to the fully supervised learning methods on the CNN/DM and NYT
datasets.
