Contract Summarization System
A Contract Summarization System is a automated domain-specific document summarization system that implements a contract summarization algorithm to solve a automated contract summarization task.
- Context:
- It can range from being a Zero-Shot Contract Summarization System to being a Few-Shot Contract Summarization System to being a Supervised Contract Summarization System.
- It can range from being a Automated Single-Contract Summarization System to being a Automated Multi-Contract Summarization System.
- It can range from being an Extractive Contract-Related Summarization System to being an Abstractive Contract-Related Summarization System.
- …
- Example(s):
- Counter-Example(s):
- See: News Summarization System, Contract-Related Query-Replying System, Prompt Engineering System, Natural Language Processing System, Contract Management System, Contract Analysis System, Legal Contract Dataset.
References
2023
- (ChatGPT-OpenAI, 2023) ⇒ https://chat.openai.com/ 2023-09-17.
- Q: What is a Contract Summarization System?
- A: A Contract Summarization System is a technology-driven solution designed to automatically condense and extract key information from legal contracts and agreements. It leverages natural language processing (NLP) and machine learning techniques to analyze the content of contracts and generate concise summaries that capture the most critical terms, clauses, and obligations within the document. This system is particularly valuable in the legal and business sectors, where contracts can be lengthy and complex, making it time-consuming for professionals to review and understand them in detail.
The process typically involves the system parsing the contract's text, identifying relevant sections, and then generating a summary that highlights essential provisions such as payment terms, obligations of each party, termination clauses, dispute resolution mechanisms, and any significant liabilities or restrictions. Contract summarization systems aim to improve efficiency and accuracy in contract management, due diligence, and compliance efforts by providing a quick overview of contractual obligations and potential risks. These summaries can assist legal professionals, business executives, and other stakeholders in making informed decisions and managing contractual relationships more effectively.
2022
- (Sancheti et al., 2022) ⇒ Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, and Rachel Rudinger(2022). “What to Read in a Contract? Party-Specific Summarization of Important Obligations, Entitlements, and Prohibitions in Legal Documents. In: arXiv:2212.09825.
- QUOTE: This work makes the following contributions: (a) we propose an extractive summarization system (§3), CONTRASUM, to summarize the key obligations, entitlements, and prohibitions mentioned in a contract for each of the parties; (b) we introduce a dataset (§4) consisting of comparative importance annotations for sentences (that include obligations, entitlements, or prohibitions) in lease agreements, with respect to each of the parties; and (c) we perform automatic (§7) and human evaluation (§8) of our system against several unsupervised summarization methods to demonstrate the effectiveness and usefulness of the system. To the best of our knowledge, ours is the first work to collect pairwise importance comparison annotations for sentences in contracts and use it for obtaining summaries for legal contracts.
2019
- (Manor & Li, 2019) ⇒ Laura Manor, and Junyi Jessy Li (2019). "Plain English Summarization of Contracts" ArXiv:/abs/1906.00424.
- QUOTE: We propose the task of the automatic summarization of legal documents in plain English for a non-legal audience. We hope that such a technological advancement would enable a greater number of people to enter into everyday contracts with a better understanding of what they are agreeing to. (...)
Rather than attempt to summarize an entire document, these sources summarize each document at the section level. In this way, the reader can reference the more detailed text if need be. The summaries in this dataset are reviewed for quality by the first author, who has 3 years of professional contract drafting experience. The dataset we propose contains 446 sets of parallel text. We show the level of abstraction through the number of novel words in the reference summaries, which is significantly higher than the abstractive single-document summaries created for the shared tasks of the Document Understanding Conference (DUC) in 2002 [1], a standard dataset used for single document news summarization. Additionally, we utilize several common readability metrics to show that there is an average of a 6 year reading level difference between the original documents and the reference summaries in our legal dataset.
In initial experimentation using this dataset, we employ popular unsupervised extractive summarization models such as TextRank [2] and Greedy KL [3], as well as lead baselines. We show that such methods do not perform well on this dataset when compared to the same methods on DUC 2002. These results highlight the fact that this is a very challenging task. As there is not currently a dataset in this domain large enough for supervised methods, we suggest the use of methods developed for simplification and/or style transfer(...)
- QUOTE: We propose the task of the automatic summarization of legal documents in plain English for a non-legal audience. We hope that such a technological advancement would enable a greater number of people to enter into everyday contracts with a better understanding of what they are agreeing to. (...)
- ↑ (Over et al., 2007) ⇒ Paul Over, Hoa Dang, and Donna Harman (2007). “DUC in Context". In: Information Processing & Management, 43(6):1506–1520.
- ↑ (Mihalcea & Tarau, 2004) ⇒ Rada Mihalcea, and Paul Tarau (2004). “Textrank: Bringing Order into Text". In: Proceedings of the 2004 conference on empirical methods in natural language processing.
- ↑ (Haghighi & Vanderwende, 2009) ⇒ Aria Haghighi, and Lucy Vanderwende (2009). “Exploring content models for multi-document summarization. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 362–370.