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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| Online-Zugang: | https://arxiv.org/abs/2312.05172 |
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| _version_ | 1866910006820995072 |
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| author | Passali, Tatiana Chatzikyriakidis, Efstathios Andreadis, Stelios Stavropoulos, Thanos G. Matonaki, Anastasia Fachantidis, Anestis Tsoumakas, Grigorios |
| author_facet | Passali, Tatiana Chatzikyriakidis, Efstathios Andreadis, Stelios Stavropoulos, Thanos G. Matonaki, Anastasia Fachantidis, Anestis Tsoumakas, Grigorios |
| contents | 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05172 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences Passali, Tatiana Chatzikyriakidis, Efstathios Andreadis, Stelios Stavropoulos, Thanos G. Matonaki, Anastasia Fachantidis, Anestis Tsoumakas, Grigorios Computation and Language 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 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. |
| title | From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2312.05172 |