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

TL;DR

This paper introduces a unified approach combining neural dependency parsing and graph-based NLP to extract keyphrases, summaries, and relations, enabling interactive content retrieval with a focus on relevant sentence elements.

Contribution

It presents a novel method that reorganizes dependency graphs for better extraction of key content and integrates this into a dialog engine for interactive document exploration.

Findings

Effective extraction of keyphrases and summaries from dependency graphs

Enhanced document relevance through query-specific graph refinement

Open-source implementation available for further research

Abstract

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.