Dependency-based Text Graphs for Keyphrase and Summary Extraction with
  Applications to Interactive Content Retrieval
  We build a bridge between neural network-based machine learning and
graph-based natural language processing and introduce a unified approach to
keyphrase, summary and relation extraction by aggregating dependency graphs
from links provided by a deep-learning based dependency parser.
  We reorganize dependency graphs to focus on the most relevant content
elements of a sentence, integrate sentence identifiers as graph nodes and after
ranking the graph, we extract our keyphrases and summaries from its largest
strongly-connected component. We take advantage of the implicit structural
information that dependency links bring to extract subject-verb-object, is-a
and part-of relations.
  We put it all together into a proof-of-concept dialog engine that specializes
the text graph with respect to a query and reveals interactively the document's
most relevant content elements. The open-source code of the integrated system
is available at https://github.com/ptarau/DeepRank .
  Keywords: graph-based natural language processing, dependency graphs,
keyphrase, summary and relation extraction, query-driven salient sentence
extraction, logic-based dialog engine, synergies between neural and symbolic
processing.
