@inproceedings{adibhatla-shrivastava-2022-scone,
    title = "{SC}on{E}:Contextual Relevance based {S}ignificant {C}ompo{N}ent {E}xtraction from Contracts",
    author = "Adibhatla, Hiranmai  and
      Shrivastava, Manish",
    editor = "Akhtar, Md. Shad  and
      Chakraborty, Tanmoy",
    booktitle = "Proceedings of the 19th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2022",
    address = "New Delhi, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2022.icon-main.22/",
    pages = "161--171",
    abstract = "Automatic extraction of {\textquotedblleft}significant{\textquotedblright} components of a legal contract, has the potential to simplify the end user{'}s comprehension. In essence, {\textquotedblleft}significant{\textquotedblright} pieces of information have 1) information pertaining to material/practical details about a specific contract and 2) information that is novel or comes as a {\textquotedblleft}surprise{\textquotedblright} for a specific type of contract. It indicates that the significance of a component may be defined at an individual contract level and at a contract-type level. A component, sentence, or paragraph, may be considered significant at a contract level if it contains contract-specific information (CSI), like names, dates, or currency terms. At a contract-type level, components that deviate significantly from the norm for the type may be considered significant (type-specific information (TSI)). In this paper, we present approaches to extract {\textquotedblleft}significant{\textquotedblright} components from a contract at both these levels. We attempt to do this by identifying patterns in a pool of documents of the same kind. Consequently, in our approach, the solution is formulated in two parts: identifying CSI using a BERT-based contract-specific information extractor and identifying TSI by scoring sentences in a contract for their likelihood. In this paper, we even describe the annotated corpus of contract documents that we created as a first step toward the development of such a language-processing system. We also release a dataset of contract samples containing sentences belonging to CSI and TSI."
}