@inproceedings{abbasi-etal-2025-neural,
    title = "Neural Document Segmentation Using Weighted Sliding Windows with Transformer Encoders",
    author = "Abbasi, Saeed  and
      An, Aijun  and
      Davoudi, Heidar  and
      Di Carlantonio, Ron  and
      Farmaner, Gary",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven  and
      Darwish, Kareem  and
      Agarwal, Apoorv",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-industry.67/",
    pages = "807--816",
    abstract = "We introduce a novel Transformer-based method for document segmentation, tailored for practical, real-world applications. This method utilizes overlapping text sequences with a unique position-aware weighting mechanism to enhance segmentation accuracy. Through comprehensive experiments on both public and proprietary datasets, we demonstrate significant improvements, establishing new state-of-the-art standards by achieving up to a 10{\%} increase in segmentation F1 score compared to existing methods. Additionally, we explore the application of our segmentation method in downstream retrieval-augmented question answering tasks, where it improves the quality of generated responses by 5{\%} while achieving up to four times greater efficiency. These results underscore our model{'}s potential as a robust and scalable solution for real-world text segmentation challenges."
}