mlsanityData to 7 Oct 2026

What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions

arXiv 2212.09825 ↗First posted 19 Dec 2022cs.CLcited by 5

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Abstract (arXiv)

Reviewing and comprehending key obligations, entitlements, and prohibitions in legal contracts can be a tedious task due to their length and domain-specificity. Furthermore, the key rights and duties requiring review vary for each contracting party. In this work, we propose a new task of party-specific extractive summarization for legal contracts to facilitate faster reviewing and improved comprehension of rights and duties. To facilitate this, we curate a dataset comprising of party-specific pairwise importance comparisons annotated by legal experts, covering ~293K sentence pairs that include obligations, entitlements, and prohibitions extracted from lease agreements. Using this dataset, we train a pairwise importance ranker and propose a pipeline-based extractive summarization system that generates a party-specific contract summary. We establish the need for incorporating domain-specific notion of importance during summarization by comparing our system against various baselines using both automatic and human evaluation methods

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 51 references that are in the map. First posted on arXiv 19 Dec 2022.

Builds on

15 of 51 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  4. 2016Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translationcited by 467
  5. 2017A Deep Reinforced Model for Abstractive Summarizationcited by 364
  6. 2017Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotationcited by 77
  7. 2019Sentence Centrality Revisited for Unsupervised Summarizationcited by 64
  8. 2018Efficient Online Scalar Annotation with Bounded Supportcited by 27
  9. 2018LexNLP: Natural language processing and information extraction for legal and regulatory textscited by 27
  10. 2022ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Miningcited by 25
  11. 2021Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documentscited by 23
  12. 2018Obligation and Prohibition Extraction Using Hierarchical RNNscited by 18
3 more
  1. 2021What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Studycited by 15
  2. 2022Extractive Summarization of Legal Decisions using Multi-task Learning and Maximal Marginal Relevancecited by 9
  3. 2022Agent-Specific Deontic Modality Detection in Legal Languagecited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 1; 2026: 1.

5 papers.