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Graph-based Keyword Planning for Legal Clause Generation from Topics

arXiv 2301.06901 ↗First posted 7 Jan 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 7 Jan 2023.

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9 of 19 references.

References · 9

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
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  7. 2021EDGAR-CORPUS: Billions of Tokens Make The World Go Roundcited by 32
  8. 2021GraphPlan: Story Generation by Planning with Event Graphcited by 18
  9. 2021CLAUSEREC: A Clause Recommendation Framework for AI-aided Contract Authoringcited by 4

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