@inproceedings{pant-chopra-2022-multilingual,
    title = "Multilingual Financial Documentation Summarization by {T}eam{\_}{T}redence for {FNS}2022",
    author = "Pant, Manish  and
      Chopra, Ankush",
    editor = "El-Haj, Mahmoud  and
      Rayson, Paul  and
      Zmandar, Nadhem",
    booktitle = "Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.fnp-1.18/",
    pages = "112--115",
    abstract = "This paper describes multi-lingual long document summarization systems submitted to the Financial Narrative Summarization Shared Task (FNS 2022 ) by Team-Tredence. We developed task-specific summarization methods for 3 languages {--} English, Spanish and Greek. The solution is divided into two parts, where a RoBERTa model was finetuned to identify/extract summarizing segments from English documents and T5 based models were used for summarizing Spanish and Greek documents. A purely extractive approach was applied to summarize English documents using data-specific heuristics. An mT5 model was fine-tuned to identify potential narrative sections for Greek and Spanish, followed by finetuning mT5 and T5(Spanish version) for abstractive summarization task. This system also features a novel approach for generating summarization training dataset using long document segmentation and the semantic similarity across segments. We also introduce an N-gram variability score to select sub-segments for generating more diverse and informative summaries from long documents."
}