@inproceedings{chatterjee-etal-2022-summarization,
    title = "Summarization of Long Input Texts Using Multi-Layer Neural Network",
    author = "Chatterjee, Niladri  and
      Khatri, Aadyant  and
      Agarwal, Raksha",
    editor = "Mckeown, Kathleen",
    booktitle = "Proceedings of The Workshop on Automatic Summarization for Creative Writing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.creativesumm-1.2",
    pages = "13--18",
    abstract = "This paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multilayer network is constructed by establishing linkages between the different parts. During training phases, several hyperparameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest.",
}