Semantic Similarity Measure of Natural Language Text through Machine
  Learning and a Keyword-Aware Cross-Encoder-Ranking Summarizer -- A Case Study
  Using UCGIS GIS&T Body of Knowledge
Yuanyuan Tian Wenwen Li Sizhe Wang Zhining Gu
Initiated by the University Consortium of Geographic Information Science
(UCGIS), GIS&T Body of Knowledge (BoK) is a community-driven endeavor to
define, develop, and document geospatial topics related to geographic
information science and technologies (GIS&T). In recent years, GIS&T BoK has
undergone rigorous development in terms of its topic re-organization and
content updating, resulting in a new digital version of the project. While the
BoK topics provide useful materials for researchers and students to learn about
GIS, the semantic relationships among the topics, such as semantic similarity,
should also be identified so that a better and automated topic navigation can
be achieved. Currently, the related topics are either defined manually by
editors or authors, which may result in an incomplete assessment of topic
relationship. To address this challenge, our research evaluates the
effectiveness of multiple natural language processing (NLP) techniques in
extracting semantics from text, including both deep neural networks and
traditional machine learning approaches. Besides, a novel text summarization -
KACERS (Keyword-Aware Cross-Encoder-Ranking Summarizer) - is proposed to
generate a semantic summary of scientific publications. By identifying the
semantic linkages among key topics, this work provides guidance for future
development and content organization of the GIS&T BoK project. It also offers a
new perspective on the use of machine learning techniques for analyzing
scientific publications, and demonstrate the potential of KACERS summarizer in
semantic understanding of long text documents.