Dependency-based Text Graphs for Keyphrase and Summary Extraction with Applications to Interactive Content Retrieval
This work addresses the need for efficient text analysis and retrieval in natural language processing, though it appears incremental as it builds on existing graph-based and neural methods.
The authors tackled the problem of extracting keyphrases, summaries, and relations from text by integrating dependency graphs from a deep-learning parser, resulting in a unified approach that enables interactive content retrieval through a proof-of-concept dialog engine.
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.