  We construct a contextual network to represent a document with syntactic and
semantic relations between word-sentence pairs, based on which we devise an
unsupervised algorithm called CNATAR (Contextual Network And Text Analysis
Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack
maximization problem over topic analysis and sentence scores. We show that
CNATAR outperforms the combined ranking of the three human judges provided on
the SummBank dataset under both ROUGE and BLEU metrics, which in term
significantly outperforms each individual judge's ranking. Moreover, CNATAR
produces so far the highest ROUGE scores over DUC-02, and outperforms previous
supervised algorithms on the CNN/DailyMail and NYT datasets. We also compare
the performance of CNATAR and the latest supervised neural-network
summarization models and compute oracle results.
