Multi-document Summarization by Graph Search and Matching
Inderjeet Mani (MITRE Corporation), Eric Bloedorn (MITRE Corporation)

TL;DR
This paper introduces a novel graph-based method for multi-document summarization that captures similarities and differences by representing text as semantic graphs and matching them to generate natural language summaries.
Contribution
It presents a new approach combining graph representations and spreading activation for summarizing related documents, emphasizing semantic relations and differences.
Findings
Effective identification of semantic similarities and differences
Natural language summaries generated from graph matching
Evaluation shows promising results in summarization quality
Abstract
We describe a new method for summarizing similarities and differences in a pair of related documents using a graph representation for text. Concepts denoted by words, phrases, and proper names in the document are represented positionally as nodes in the graph along with edges corresponding to semantic relations between items. Given a perspective in terms of which the pair of documents is to be summarized, the algorithm first uses a spreading activation technique to discover, in each document, nodes semantically related to the topic. The activated graphs of each document are then matched to yield a graph corresponding to similarities and differences between the pair, which is rendered in natural language. An evaluation of these techniques has been carried out.
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Semantic Web and Ontologies
