Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks

TL;DR

This paper introduces a multilayer network approach to quantitatively analyze discourse cohesion in scientific texts, providing metrics and a framework to help authors improve writing quality by identifying cohesion gaps.

Contribution

It presents novel section- and document-level cohesion metrics based on multilayer networks and a framework, CHIAA, to guide authors in enhancing scholarly writing.

Findings

Proposed metrics correlate well with existing cohesion indices.

CHIAA framework effectively identifies cohesion gaps in texts.

Validated approach using a dataset with contrasting examples.

Abstract

Discourse cohesion facilitates text comprehension and helps the reader form a coherent narrative. In this study, we aim to computationally analyze the discourse cohesion in scientific scholarly texts using multilayer network representation and quantify the writing quality of the document. Exploiting the hierarchical structure of scientific scholarly texts, we design section-level and document-level metrics to assess the extent of lexical cohesion in text. We use a publicly available dataset along with a curated set of contrasting examples to validate the proposed metrics by comparing them against select indices computed using existing cohesion analysis tools. We observe that the proposed metrics correlate as expected with the existing cohesion indices. We also present an analytical framework, CHIAA (CHeck It Again, Author), to provide pointers to the author for potential improvements in the manuscript with the help of the section-level and document-level metrics. The proposed CHIAA framework furnishes a clear and precise prescription to the author for improving writing by localizing regions in text with cohesion gaps. We demonstrate the efficacy of CHIAA framework using succinct examples from cohesion-deficient text excerpts in the experimental dataset.