Automated Contract-Related Summarization Task
An Automated Contract-Related Summarization Task is a automated topic-focused summarization task that is a contract summarization task.
- Context:
- It can (typically) be solved by a Contract Summarization System (that implements a contract summarization algorithm).
- It can range from being a Zero-Shot Contract Summarization Task to being a Few-Shot Contract Summarization Task to being a Supervised Contract Summarization Task.
- It can range from being a Automated Single-Contract Summarization Task to being a Automated Multi-Contract Summarization Task.
- …
- Example(s):
- an Automated Single-Contract Summarization.
- an Automated Contract Revisions Summarization (for a contract revision summary).
- an Automated Multi-Contract Summarization.
- Party-Specific Summarization of Important Obligations, Entitlements, and Prohibitions in Legal Documents Task (Sancheti et al., 2022),
- Contract Summarization Task (Manor & Li, 2019),
- ...
- Counter-Example(s):
- See: Automated Contract Summarization System, Natural Language Processing, Contract Management Task, Contract Analysis Task, Legal Contract Dataset, TextRank.
References
2024
- Ken Adams. (2024). "Something Else to Not Use AI For: Summarizing Contracts."
- NOTES:
- The article updates a previous post to provide a detailed critique of using AI for summarizing contracts, emphasizing the unchanged conclusion despite added detail.
- The article explores the inherent dysfunction in mainstream contract language and how AI, trained on such language, replicates these issues in an unpredictable manner.
- The article evaluates AI summarization through an example, comparing summaries from different versions of ChatGPT and highlighting inaccuracies and omissions.
- The article argues against the usefulness of summarizing contracts, stating that every element of a contract is critical and that summarization often leads to significant omissions or alterations.
- The article suggests that outlining contracts is a preferable alternative to summarization, offering a structured approach that highlights key elements without oversimplification.
- The article critiques AI's ability to effectively summarize or outline contracts, pointing out the limitations of AI in capturing the nuanced details necessary for accurate legal interpretation.
- The article includes specific criticisms of AI summarization, such as the omission of important references, the generalization of defined terms, and the simplification of legal standards.
- The article emphasizes the importance of direct engagement with the actual contract text to understand its implications fully, cautioning against reliance on AI for legal analysis.
- The article contributes to the broader discussion on the limitations of AI in complex, nuanced tasks like legal document interpretation, advocating for cautious integration of AI into legal practices.
- NOTES:
2023
- (ChatGPT-OpenAI, 2023) ⇒ https://chat.openai.com/ 2023-09-17.
- Q: What is an Automated Contract-Related Summarization Task?
- A: An Automated Contract-Related Summarization Task refers to the process of using artificial intelligence and natural language processing techniques to automatically generate concise and coherent summaries of complex legal contracts or documents. This task is particularly valuable in the legal industry and various business sectors where contracts play a crucial role.
In this task, the AI system reads and analyzes the content of a lengthy legal contract and then distills the most important information, terms, and provisions into a shorter, more easily understandable summary. These summaries are essential for legal professionals, business executives, and other stakeholders who need to quickly grasp the key aspects of a contract without delving into the entire document. Automated contract-related summarization can save significant time and resources, enhance contract management efficiency, and reduce the risk of overlooking critical details or obligations within a contract.
To perform this task effectively, AI models like GPT-3.5 can identify key clauses, obligations, termination conditions, payment terms, and any exceptional provisions within a contract, presenting them in a coherent and structured manner. This technology has the potential to streamline contract review and analysis processes, increase legal compliance, and improve decision-making regarding contract-related matters.
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: Therefore, we propose a system to generate party-specific summaries consisting of important obligations, entitlements, and prohibitions mentioned in a given contract. The motivation behind different categories of the summary comes from the software license summaries available at TL; DRLegal [1] which describe what users must, can, and cannot do under the license. We first identify all the sentences containing obligations, entitlements, and prohibitions in a given contract with respect to a party, using a content categorizer (§3.1). Then, the identified sentences are ranked based on their importance (e.g., any maintenance or repairs that a tenant is required to do at its expense is more important than delivering insurance certificate to the landlord on a specified date) using an importance ranker (§3.2) trained on a legal expert-annotated dataset that we collect[2] (§4) to quantify the notion of importance. We believe our two-staged approach is less expensive to train compared to training an end-to-end summarization system which would require summaries to be annotated for long contracts (spanning 10 − 100 pages).
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.
- QUOTE: Therefore, we propose a system to generate party-specific summaries consisting of important obligations, entitlements, and prohibitions mentioned in a given contract. The motivation behind different categories of the summary comes from the software license summaries available at TL; DRLegal [1] which describe what users must, can, and cannot do under the license. We first identify all the sentences containing obligations, entitlements, and prohibitions in a given contract with respect to a party, using a content categorizer (§3.1). Then, the identified sentences are ranked based on their importance (e.g., any maintenance or repairs that a tenant is required to do at its expense is more important than delivering insurance certificate to the landlord on a specified date) using an importance ranker (§3.2) trained on a legal expert-annotated dataset that we collect[2] (§4) to quantify the notion of importance. We believe our two-staged approach is less expensive to train compared to training an end-to-end summarization system which would require summaries to be annotated for long contracts (spanning 10 − 100 pages).
- ↑ https://www.tldrlegal.com
- ↑ We will publicly release this dataset.
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.