From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences
Tatiana Passali, Efstathios Chatzikyriakidis, Stelios Andreadis, Thanos G. Stavropoulos, Anastasia Matonaki, Anestis Fachantidis, Grigorios Tsoumakas

TL;DR
This paper systematically reviews NLP techniques for simplifying long sentences through compression and splitting, highlighting trends, current methods, gaps, and future research opportunities to improve readability and communication.
Contribution
It provides a comprehensive taxonomy, comparative evaluation, and analysis of NLP methods for long sentence simplification, emphasizing the need for weakly supervised and LLM-based approaches.
Findings
Supervised methods dominate current research.
Significant growth in interest since 2017.
Notable gap in weakly and self-supervised techniques.
Abstract
Long sentences have been a persistent issue in written communication for many years since they make it challenging for readers to grasp the main points or follow the initial intention of the writer. This survey, conducted using the PRISMA guidelines, systematically reviews two main strategies for addressing the issue of long sentences: a) sentence compression and b) sentence splitting. An increased trend of interest in this area has been observed since 2005, with significant growth after 2017. Current research is dominated by supervised approaches for both sentence compression and splitting. Yet, there is a considerable gap in weakly and self-supervised techniques, suggesting an opportunity for further research, especially in domains with limited data. We also observe that despite their potential, Large Language Models (LLMs) have not yet been widely explored in this area. In this…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Text Readability and Simplification
