# Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks

arXiv: https://arxiv.org/abs/2205.07532 · HTML: https://mlsanity.com/paper/2205.07532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 72 references that are in the map.
- First posted on arXiv 16 May 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications](https://mlsanity.com/paper/1804.09635/) (2018)
- [The structure and dynamics of multilayer networks](https://mlsanity.com/paper/1407.0742/) (2014)
- [Comparing the writing style of real and artificial papers](https://mlsanity.com/paper/1506.05702/) (2015)
- [Extractive Multi-document Summarization Using Multilayer Networks](https://mlsanity.com/paper/1711.02608/) (2017)
- [sCAKE: Semantic Connectivity Aware Keyword Extraction](https://mlsanity.com/paper/1811.10831/) (2018)
- [Complex Network based Supervised Keyword Extractor](https://mlsanity.com/paper/1909.12009/) (2019)
- [On the role of words in the network structure of texts: application to authorship attribution](https://mlsanity.com/paper/1705.04187/) (2017)
- [Multilayer Networks for Text Analysis with Multiple Data Types](https://mlsanity.com/paper/2106.15821/) (2021)

## Built on by (most cited first, 2 of 2)

- [Improving Attributed Long-form Question Answering with Intent Awareness](https://mlsanity.com/paper/2603.27435/) (2026)
- [Evaluating Discourse Cohesion in Pre-trained Language Models](https://mlsanity.com/paper/2503.06137/) (2025)

## Abstract (arXiv)

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.