  Recently, the seq2seq abstractive summarization models have achieved good
results on the CNN/Daily Mail dataset. Still, how to improve abstractive
methods with extractive methods is a good research direction, since extractive
methods have their potentials of exploiting various efficient features for
extracting important sentences in one text. In this paper, in order to improve
the semantic relevance of abstractive summaries, we adopt the WordNet based
sentence ranking algorithm to extract the sentences which are most semantically
to one text. Then, we design a dual attentional seq2seq framework to generate
summaries with consideration of the extracted information. At the same time, we
combine pointer-generator and coverage mechanisms to solve the problems of
out-of-vocabulary (OOV) words and duplicate words which exist in the
abstractive models. Experiments on the CNN/Daily Mail dataset show that our
models achieve competitive performance with the state-of-the-art ROUGE scores.
Human evaluations also show that the summaries generated by our models have
high semantic relevance to the original text.
