Abstract
Automatic extraction of “significant” components of a legal contract, has the potential to simplify the end user’s comprehension. In essence, “significant” 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 “surprise” 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 “significant” 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.- Anthology ID:
- 2022.icon-main.22
- Volume:
- Proceedings of the 19th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2022
- Address:
- New Delhi, India
- Editors:
- Md. Shad Akhtar, Tanmoy Chakraborty
- Venue:
- ICON
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 161–171
- Language:
- URL:
- https://aclanthology.org/2022.icon-main.22
- DOI:
- Cite (ACL):
- Hiranmai Adibhatla and Manish Shrivastava. 2022. SConE:Contextual Relevance based Significant CompoNent Extraction from Contracts. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 161–171, New Delhi, India. Association for Computational Linguistics.
- Cite (Informal):
- SConE:Contextual Relevance based Significant CompoNent Extraction from Contracts (Adibhatla & Shrivastava, ICON 2022)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2022.icon-main.22.pdf