Multi-document Cohesion Network Analysis: Visualizing Intratextual and Intertextual Links

Maria Dorinela Dascalu, Stefan Ruseti, Mihai Dascalu, Danielle S. McNamara, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Reading comprehension requires readers to connect ideas within and across texts to produce a coherent mental representation. One important factor in that complex process regards the cohesion of the document(s). Here, we tackle the challenge of providing researchers and practitioners with a tool to visualize text cohesion both within (intra) and between (inter) texts. This tool, Multi-document Cohesion Network Analysis (MD-CNA), expands the structure of a CNA graph with lexical overlap links of multiple types, together with coreference links to highlight dependencies between text fragments of different granularities. We introduce two visualizations of the CNA graph that support the visual exploration of intratextual and intertextual links. First, a hierarchical view displays a tree-structure of discourse as a visual illustration of CNA links within a document. Second, a grid view available at paragraph or sentence levels displays links both within and between documents, thus ensuring ease of visualization for links spanning across multiple documents. Two use cases are provided to evaluate key functionalities and insights for each type of visualization.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 21st International Conference, AIED 2020, Proceedings
EditorsIg Ibert Bittencourt, Mutlu Cukurova, Rose Luckin, Kasia Muldner, Eva Millán
Number of pages6
ISBN (Print)9783030522391
StatePublished - 2020
Event21st International Conference on Artificial Intelligence in Education, AIED 2020 - Ifrane, Morocco
Duration: Jul 6 2020Jul 10 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12164 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Artificial Intelligence in Education, AIED 2020


  • Cohesion Network Analysis
  • Coreference links
  • Graph visualizations
  • Lexical overlap links
  • Semantic links

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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