The spread of misinformation online, most commonly known as fake news, is an important issue that has become more pronounced in the last two decades due to the prevalence of social media. Platforms like Twitter, Reddit, and Facebook, have been commonly identified as the main channels for propagating misinformation and have been criticized for not acting on addressing the conditions that permit the circulation and amplification of false information [32]. Such misinformation includes false claims and non fact-checked news items, that originate from sources of questionable credibility [113]. The problem of misinformation becomes critical when it pertains to healthcare and health issues, since it puts lives and the public health at risk. One of the first cases of widely spread misinformation in the medical domain is the falsehood that the MMR vaccine (Measles, Mumps, Rubella) causes autism [109]. The falsehood originated from a fraudulent article titled "Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children" published in the prestigious Lancet journal in 1998 [171, 197]. This study turned tens of thousands of parents against the vaccine, and as a result, in 2020, many countries, including the United Kingdom, Greece, Venezuela, and Brazil, lost their measles elimination status. In 2020, twenty-two years after publishing this study Lancet retracted the paper [203].

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. In this survey, we categorize and group the most representative methods into a comprehensive taxonomy. We also conduct a comparative evaluation analysis of these methods on common sentence compression and splitting datasets. Finally, we discuss the challenges and limitations of current methods, providing valuable insights for future research directions. This survey is meant to serve as a comprehensive resource for addressing the complexities of long sentences. We aim to enable researchers to make further advancements in the field until long sentences are no longer a barrier to effective communication.