LitStoryTeller+: an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox

Qing Ping, Chaomei Chen

Research output: Contribution to journalArticle

Abstract

The continuing growth of scientific publications has posed a double-challenge to researchers, to not only grasp the overall research trends in a scientific domain, but also get down to research details embedded in a collection of core papers. Existing work on science mapping provides multiple tools to visualize research trends in domain on macro-level, and work from the digital humanities have proposed text visualization of documents, topics, sentences, and words on micro-level. However, existing micro-level text visualizations are not tailored for scientific paper corpus, and cannot support meso-level scientific reading, which aligns a set of core papers based on their research progress, before drilling down to individual papers. To bridge this gap, the present paper proposes LitStoryTeller+, an interactive system under a unified framework that can support both meso-level and micro-level scientific paper visual storytelling. More specifically, we use entities (concepts and terminologies) as basic visual elements, and visualize entity storylines across papers and within a paper borrowing metaphors from screen play. To identify entities and entity communities, named entity recognition and community detection are performed. We also employ a variety of text mining methods such as extractive text summarization and comparative sentence classification to provide rich textual information supplementary to our visualizations. We also propose a top-down story-reading strategy that best takes advantage of our system. Two comprehensive hypothetical walkthroughs to explore documents from the computer science domain and history domain with our system demonstrate the effectiveness of our story-reading strategy and the usefulness of LitStoryTeller+.

Original languageEnglish
Pages (from-to)1887-1944
Number of pages58
JournalScientometrics
Volume116
Issue number3
DOIs
Publication statusPublished - 2018 Sep 1

Fingerprint

micro level
visualization
meso level
Visualization
trend
Terminology
macro level
computer science
technical language
Computer science
community
Macros
metaphor
Drilling
history
science

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Computer Science Applications
  • Library and Information Sciences

Cite this

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abstract = "The continuing growth of scientific publications has posed a double-challenge to researchers, to not only grasp the overall research trends in a scientific domain, but also get down to research details embedded in a collection of core papers. Existing work on science mapping provides multiple tools to visualize research trends in domain on macro-level, and work from the digital humanities have proposed text visualization of documents, topics, sentences, and words on micro-level. However, existing micro-level text visualizations are not tailored for scientific paper corpus, and cannot support meso-level scientific reading, which aligns a set of core papers based on their research progress, before drilling down to individual papers. To bridge this gap, the present paper proposes LitStoryTeller+, an interactive system under a unified framework that can support both meso-level and micro-level scientific paper visual storytelling. More specifically, we use entities (concepts and terminologies) as basic visual elements, and visualize entity storylines across papers and within a paper borrowing metaphors from screen play. To identify entities and entity communities, named entity recognition and community detection are performed. We also employ a variety of text mining methods such as extractive text summarization and comparative sentence classification to provide rich textual information supplementary to our visualizations. We also propose a top-down story-reading strategy that best takes advantage of our system. Two comprehensive hypothetical walkthroughs to explore documents from the computer science domain and history domain with our system demonstrate the effectiveness of our story-reading strategy and the usefulness of LitStoryTeller+.",
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LitStoryTeller+ : an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox. / Ping, Qing; Chen, Chaomei.

In: Scientometrics, Vol. 116, No. 3, 01.09.2018, p. 1887-1944.

Research output: Contribution to journalArticle

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