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We build a bridge between neural network-based machine learning andgraph-based natural language processing and introduce a unified approach tokeyphrase, summary and relation extraction by aggregating dependency graphsfrom links provided by a deep-learning based dependency parser.  We reorganize dependency graphs to focus on the most relevant contentelements of a sentence, integrate sentence identifiers as graph nodes and afterranking the graph, we extract our keyphrases and summaries from its largeststrongly-connected component. We take advantage of the implicit structuralinformation that dependency links bring to extract subject-verb-object, is-aand part-of relations.  We put it all together into a proof-of-concept dialog engine that specializesthe text graph with respect to a query and reveals interactively the document'smost relevant content elements. The open-source code of the integrated systemis available at https://github.com/ptarau/DeepRank .  Keywords: graph-based natural language processing, dependency graphs,keyphrase, summary and relation extraction, query-driven salient sentenceextraction, logic-based dialog engine, synergies between neural and symbolicprocessing.

Dependency-based Text Graphs for Keyphrase and Summary Extraction with Applications to Interactive Content Retrieval