  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.
