mlsanityData to 14 Oct 2026

STRONG -- Structure Controllable Legal Opinion Summary Generation

arXiv 2309.17280 ↗First posted 29 Sep 2023cs.CL cs.AIcited by 3

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Abstract (arXiv)

We propose an approach for the structure controllable summarization of long legal opinions that considers the argument structure of the document. Our approach involves using predicted argument role information to guide the model in generating coherent summaries that follow a provided structure pattern. We demonstrate the effectiveness of our approach on a dataset of legal opinions and show that it outperforms several strong baselines with respect to ROUGE, BERTScore, and structure similarity.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 34 references that are in the map. First posted on arXiv 29 Sep 2023.

Builds on

25 of 34 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  5. 2020Longformer: The Long-Document Transformercited by 3,413
  6. 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,282
  7. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 584
  8. 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 407
  9. 2017Toward Controlled Generation of Textcited by 272
  10. 2021When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Datasetcited by 201
  11. 2019Neural Text Summarization: A Critical Evaluationcited by 189
  12. 2020GSum: A General Framework for Guided Neural Abstractive Summarizationcited by 129
13 more
  1. 2020Evaluating Factuality in Generation with Dependency-level Entailmentcited by 122
  2. 2020CTRLsum: Towards Generic Controllable Text Summarizationcited by 98
  3. 2020Neural Syntactic Preordering for Controlled Paraphrase Generationcited by 56
  4. 2021MReD: A Meta-Review Dataset for Structure-Controllable Text Generationcited by 42
  5. 2022Extractive is not Faithful: An Investigation of Broad Unfaithfulness Problems in Extractive Summarizationcited by 32
  6. 2023Faithfulness-Aware Decoding Strategies for Abstractive Summarizationcited by 31
  7. 2022ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Miningcited by 25
  8. 2021DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformercited by 14
  9. 2022SentBS: Sentence-level Beam Search for Controllable Summarizationcited by 10
  10. 2022Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisionscited by 8
  11. 2023Sequentially Controlled Text Generationcited by 5
  12. 2021Discourse-Aware Soft Prompting for Text Generationcited by 4
  13. 2017Object-oriented Neural Programming (OONP) for Document Understandingcited by 3

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025A Comprehensive Survey on Legal Summarization: Challenges and Future Directionscited by 6
  2. 2024How Private are Language Models in Abstractive Summarization?cited by 2
  3. 2025DiscoSum: Discourse-aware News Summarizationcited by 0