Pith. sign in

REVIEW 4 major objections 5 minor 98 references

CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper argues that legal case summarization improves when content selection and an event-based plan are explicit pipeline stages rather than implicit steps.

desk verdict A solid modular pipeline for legal summarization whose headline event-vs-entity claim is only tested with oracle plans, not the deployed one. read the letter →

arxiv 2501.14112 v1 pith:H5IQFHQF submitted 2025-01-23 cs.CL

T.Y.S.S. Santosh , Youssef Farag , Matthias Grabmair This is my paper · ORCID
classification cs.CL
keywords legalcasesummarizationcontentplanningevent-centricrepresentationsubject-verb-objecttriplesextract-then-abstractfaithfulnesscoherencelong-document
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

CoPERLex claims that summarizing legal judgments should be decomposed into three explicit stages rather than left to a single end-to-end model: first pick the sentences that matter, then write an ordered plan of what happened in the form of subject-verb-object triples, and only then generate the summary from the selected content plus the plan. The authors argue that legal texts are narratives, so an event-centric plan supplies the structure that end-to-end models lack. On four legal summarization datasets, they report that this pipeline improves faithfulness and coherence over strong long-document baselines, and that event-based plans outperform entity-based plans. If the claim holds, legal summarization becomes more reliable and more controllable, because a user can inspect and edit the plan before the summary is written.

What carries the argument

The load-bearing object is the content plan: an ordered list of events, each written as a subject-verb-object triple, extracted from reference summaries and generated from the selected salient sentences. This plan is what connects the content-selection stage to the summary-generation stage; it tells the generator what happened, in what order, so the final text follows the case's narrative rather than drifting or inventing details. The pipeline also uses a hybrid training scheme that mixes oracle-selected content and model-selected content to reduce exposure bias.

What would settle it

Shuffle the order of the subject-verb-object triples in a fixed plan and regenerate summaries on a held-out set; if faithfulness and coherence scores do not worsen, then the structured ordering of events is not what produces the reported gains.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that explicit content selection and explicit content planning are not optional scaffolding but the main sources of quality in legal summarization, and that the plan should be event-centric. The authors build a three-stage system whose intermediate representation is an ordered sequence of subject-verb-object triples: a reward-driven extractor chooses salient sentences, a long-input encoder-decoder generates the triple plan from them, and a second encoder-decoder writes the final summary conditioned on both. They report consistent gains over five long-document summarization baselines on four datasets, with the largest gains in faithfulness and coherence, and their ablations show that removing either the selection stage or the planning stage degrades performance. In a direct comparison of plan representations, the event-triple plans beat entity-chain plans, which the authors attribute to the narrative, action-driven nature of legal cases.

Load-bearing premise

The load-bearing premise is that the automatic metrics used, lexical overlap, alignment-based faithfulness, and learned coherence and fluency scores, genuinely capture the quality that matters in legal summaries, since no legal experts judged the outputs.

Editorial extensions

If this is right

  • Summarization systems for long legal documents can be built as transparent pipelines, with content selection acting as a filter that keeps the generator from being overwhelmed by irrelevant material.
  • Event plans give users a handle on output: editing or deleting a subject-verb-object triple changes the final summary in a predictable way, enabling customized and more concise summaries.
  • Because the plan representation is a generic triple rather than a jurisdiction-specific taxonomy, the same machinery should transfer to new legal systems and languages without re-engineering the plan format.
  • Faithfulness checking can be automated at the plan level by verifying that each event in the summary matches an event in the source, rather than comparing full texts.
  • The approach is most valuable where summaries must condense long, multi-source cases; on shorter-input datasets the gap over end-to-end models is smaller, so the planning overhead earns its keep mainly in the long-document regime.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Not tested here: the event-plan bottleneck could act as a cross-lingual bridge, allowing a summary to be generated in a different language from the source by translating or relexicalizing the plan.
  • The framework's gains may come partly from extractive pre-filtering and partly from the event structure; an experiment that fixes content selection and varies only the plan type would separate these contributions more cleanly than the current ablations.
  • The same planning idea could transfer to other narrative-dense document genres, such as medical records or financial rulings, if their summaries also follow event sequences.
  • Because the plan is inspectable, a practical error-correction loop becomes possible: automatically spot an unfaithful triple, replace it, and regenerate the summary without retraining the generator.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes CoPERLex, a three-stage framework for legal case summarization: MemSum performs extractive content selection; an Unlimiformer model generates an event-centric plan, represented as Subject-Verb-Object tuples extracted from gold summaries with Plumber; and a Longformer Encoder-Decoder generates the final summary conditioned on the selected content and the predicted plan. The system is evaluated on four legal summarization datasets (MultiLexSum-Long/Short, CanLII, and SuperSCOTUS) against long-document abstractive baselines, with ablations, a plan-representation comparison, a content-selection comparison, and case studies. The central claim is that integrating content selection and planning improves faithfulness and coherence, and that event-centric plans outperform entity-centric plans in legal summarization.

Significance. If validated, the paper would provide a concrete demonstration that modular planning helps long legal summarization, and that event-based representations are better suited to the narrative structure of legal texts than entity chains. The strengths of the submission include evaluation on four datasets, statistical significance tests in Table 2, an oracle-plan representation comparison in Table 4, ablations for each pipeline component, and a case study illustrating plan controllability. The main weakness is that the headline event-versus-entity claim is supported only under oracle-plan conditions, not under the actual predicted-plan pipeline, so the central advantage claimed in the abstract is not yet demonstrated.

major comments (4)
  1. [§4.1, Table 4] The abstract and conclusion claim that event-centric plans outperform entity-centric representations, but the only evidence for this is Table 4, which feeds oracle plans extracted from the reference summary to the summarizer. In the deployed pipeline, plans are predicted by Unlimiformer, whose agreement with gold plans is low (Table 6: R-1 34.96, R-2 15.65, R-L 21.44), and the summarizer receives MemSum-selected content rather than the full source document. Because oracle plans leak surface content from the reference summary, the event advantage may shrink, disappear, or reverse under plan-prediction error. An end-to-end comparison using predicted event plans versus predicted entity plans is needed to support the paper's central claim.
  2. [Table 3 and §4.1] The ablation study removes whole components (e.g., 'w/o Planning') but never holds the pipeline fixed while swapping the plan representation. Consequently, the ablations show that having some plan helps, but not that event-centric plans specifically are responsible for the gains, especially for coherence and faithfulness. Additionally, Table 3 reports no significance tests, and several differences are numerically small (e.g., CanLII ROUGE-1: 49.19 vs 49.06 for 'w/o Content Sel.'). I request an inference-time comparison with predicted entity and event plans, and significance testing for the ablations.
  3. [Table 2 and Limitations] The global claim of 'significant improvements in faithfulness and coherence' is stronger than the evidence. On MLS-Short, ROUGE-2 (23.04) and coherence (69.27) are not marked as statistically significant; on CanLII, ROUGE-1, ROUGE-2, BERTScore, and fluency are not significant. The paper should report which metrics are significant on which datasets and qualify the 'consistently outperforms' phrasing in Section 4.1. The Limitations section already concedes that AlignScore and UniEval 'are limited in their ability to fully capture the unique complexities of legal texts' and that no legal experts were available; this caveat should be reflected in the abstract and conclusion, which currently state improvements in faithfulness and coherence without qualification.
  4. [§4.1, Table 6] The text states that Unlimiformer 'consistently outperforms' LED for plan generation, but Table 6 reports a lower ROUGE-1 for Unlimiformer (34.96 vs 35.18 for LED). This is a factual contradiction in a result used to justify the choice of Unlimiformer as the plan generator. The claim should be corrected and the comparison verified; if the corrected comparison changes the architecture choice, downstream results may be affected.
minor comments (5)
  1. [Table 4] The term 'UCREAT' appears in Table 4 without being defined in the main text; please introduce the acronym or describe the event-extraction method explicitly.
  2. [Table 6] The caption 'Sum. Gen. with OP' is ambiguous: it should state explicitly whether the summarizer receives the full source document or the selected content together with the oracle plan, since this differs from the deployed setting.
  3. [Table 3] The caption abbreviates content selection as 'CS' while the text refers to 'Con. Sel.'; please align the notation and define all abbreviations in the caption.
  4. [Throughout] There are several typos, e.g., 'sentenece' in Section 3.1, 'dimenssion' in Appendix B, and 'generaiton' in the ablation-study description in Section 4.1.
  5. [§4.1, Results] The sentence saying SLED 'outperforms' the long-range models on CanLII is imprecise because the differences are small and vary by metric; please reword to say it is competitive or specify the exact metrics.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: gold plans are externally extracted, training and inference are supervised rather than definitional, and the reported comparisons rest on held-out test data rather than fitted identities.

full rationale

The paper's derivation chain is not circular. Golden content plans are produced by the external Plumber tool from reference summaries (Section 3.2: "we utilize Plumber ... to derive the event representations for each sentence in the summary"), and the plan generator and summary generator are trained with those plans as targets; the inferred plan and final summary at test time are genuine predictions, not renamed training objectives. The final system uses MemSum-selected content, Unlimiformer-predicted plans, and LED-generated summaries (Sections 3.1-3.3), so no fitted parameter or extracted oracle quantity is itself reported as the predicted summary. The event-versus-entity claim is an empirical comparison of two oracle plan representations (Table 4, MLS-Long only), not a constructional identity: entity chains and Plumber/UCREAT event triples are both extracted from reference summaries, so their downstream ROUGE difference is an experimental outcome rather than a forced equivalence. The Limitations section appropriately concedes that ROUGE, BERTScore, and AlignScore "are limited in their ability to fully capture the unique complexities of legal texts" and that no legal experts validated outputs; that is a validity limitation, not circularity. Similarly, the absence of an end-to-end predicted-plan event-vs-entity comparison (Table 4 uses oracle plans, while Table 6 shows predicted plans are far from gold) is a missing-experiment or correctness concern, not a circular reduction. The paper's self-citations (Santosh et al. 2024a,b; Tyss et al. 2024) appear only in related work and future-directions discussion and are not load-bearing for the central result. No equation equates an input with an output, no fitted parameter is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim relies on standard ML hyperparameters and on domain assumptions that automatic metrics and Plumber-extracted SVO triples are good enough to guide and evaluate legal summaries. No new physical or conceptual entities are introduced.

free parameters (5)
  • MemSum stopping threshold = 0.6
    Chosen by hand at inference to balance extraction length; affects the selected content passed to the planner.
  • Maximum extracted sentences = 45
    Limits the amount of selected content; tuned on validation performance.
  • ROUGE-2 oracle threshold for content selection = not reported
    Sentences are selected for training if their ROUGE-2 similarity to the gold summary exceeds a threshold following Liu and Chen; the exact threshold is not given.
  • Plan generator max generation length = 512 tokens
    Bounds the length of generated event plans; if too short, plans may truncate.
  • Summary generator max output length = 1024 tokens
    Defines the maximum summary length produced by the final generator.
assumptions (4)
  • domain assumption Plumber's SVO triple extraction from golden summaries yields a faithful intermediate representation of the narrative content needed for legal summaries.
    Section 3.2 uses Plumber to build golden plans and trains the plan generator on them; if triples are noisy or incomplete, the learning signal is degraded.
  • domain assumption Automatic metrics (ROUGE, BERTScore, AlignScore, UniEval) are valid proxies for summary quality, especially faithfulness and coherence, in legal text.
    Section 4 and the Limitations section; the Limitations explicitly concedes these metrics are limited and that no legal experts were involved.
  • domain assumption MemSum's ROUGE-rewarded extractive selection identifies the salient content required for a good summary.
    Section 3.1; the extractor is used at inference to feed the planner, so extraction errors propagate through the pipeline.
  • domain assumption The four datasets are representative of legal case summarization and results generalize across English legal domains.
    Section 4; the Limitations notes the evaluation is same-jurisdiction and English-only, which limits generalization.

how reviews work

0 comments
Cite this review

Pith. "Pith review of CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization." pith.science (2026). https://pith.science/paper/H5IQFHQF

@misc{pith2026250114112,
  author       = {Pith},
  title        = {Pith review of: CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H5IQFHQF}},
  note         = {Machine review of arXiv:2501.14112}
}
read the original abstract

Legal professionals often struggle with lengthy judgments and require efficient summarization for quick comprehension. To address this challenge, we investigate the need for structured planning in legal case summarization, particularly through event-centric representations that reflect the narrative nature of legal case documents. We propose our framework, CoPERLex, which operates in three stages: first, it performs content selection to identify crucial information from the judgment; second, the selected content is utilized to generate intermediate plans through event-centric representations modeled as Subject-Verb-Object tuples; and finally, it generates coherent summaries based on both the content and the structured plan. Our experiments on four legal summarization datasets demonstrate the effectiveness of integrating content selection and planning components, highlighting the advantages of event-centric plans over traditional entity-centric approaches in the context of legal judgements.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

98 extracted references · 42 canonical work pages

  1. [1]

    Griffin Adams, Alexander R Fabbri, Faisal Ladhak, Kathleen McKeown, and No \'e mie Elhadad. 2023. Generating edu extracts for plan-guided summary re-ranking. In Proceedings of the conference. Association for Computational Linguistics. Meeting, volume 2023, page 2680. NIH Public Access

    2023

  2. [2]

    Griffin Adams, Jason Zucker, and No \'e mie Elhadad. 2024. Speer: Sentence-level planning of long clinical summaries via embedded entity retrieval. arXiv preprint arXiv:2401.02369

    arXiv 2024

  3. [3]

    Abhishek Agarwal, Shanshan Xu, and Matthias Grabmair. 2022. Extractive summarization of legal decisions using multi-task learning and maximal marginal relevance. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1857--1872

    2022

  4. [4]

    Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, and Li Yang. 2020. Etc: Encoding long and structured inputs in transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 268--284

    2020

  5. [5]

    Regina Barzilay and Michael Elhadad. 1997. Using lexical chains for text summarization

    1997

  6. [6]

    Regina Barzilay and Mirella Lapata. 2008. Modeling local coherence: An entity-based approach. Computational Linguistics, 34(1):1--34

    2008

  7. [7]

    Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150

    arXiv 2020

  8. [8]

    Amanda Bertsch, Uri Alon, Graham Neubig, and Matthew Gormley. 2024. Unlimiformer: Long-range transformers with unlimited length input. Advances in Neural Information Processing Systems, 36

    2024

Show all 98 references
  1. [9]

    Amanda Bertsch, Uri Alon, Graham Neubig, and Matthew R Gormley. 2023. Unlimiformer: Long-range transformers with unlimited length input. arXiv preprint arXiv:2305.01625

    2023 arXiv

  2. [10]

    Paheli Bhattacharya, Kaustubh Hiware, Subham Rajgaria, Nilay Pochhi, Kripabandhu Ghosh, and Saptarshi Ghosh. 2019. A comparative study of summarization algorithms applied to legal case judgments. In Advances in Information Retrieval: 41st European Conference on IR Research, EC...

    2019

  3. [11]

    Paheli Bhattacharya, Soham Poddar, Koustav Rudra, Kripabandhu Ghosh, and Saptarshi Ghosh. 2021. Incorporating domain knowledge for extractive summarization of legal case documents. In Proceedings of the eighteenth international conference on artificial intelligence and law, pa...

    2021

  4. [12]

    Nathanael Chambers and Dan Jurafsky. 2008. Unsupervised learning of narrative event chains. In Proceedings of ACL-08: HLT, pages 789--797

    2008

  5. [13]

    Nathanael Chambers and Dan Jurafsky. 2009. Unsupervised learning of narrative schemas and their participants. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, pa...

    2009

  6. [14]

    Muhao Chen, Hongming Zhang, Qiang Ning, Manling Li, Heng Ji, Kathleen McKeown, and Dan Roth. 2021. Event-centric natural language processing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference ...

    2021

  7. [15]

    Hal Daum \'e III. 2009. Bayesian query-focused summarization. arXiv preprint arXiv:0907.1814

    2009 arXiv

  8. [16]

    Aniket Deroy, Kripabandhu Ghosh, and Saptarshi Ghosh. 2023 a . Ensemble methods for improving extractive summarization of legal case judgements. Artificial Intelligence and Law, pages 1--59

    2023

  9. [17]

    Aniket Deroy, Kripabandhu Ghosh, and Saptarshi Ghosh. 2023 b . How ready are pre-trained abstractive models and llms for legal case judgement summarization? arXiv preprint arXiv:2306.01248

    2023 arXiv

  10. [18]

    Aniket Deroy, Kripabandhu Ghosh, and Saptarshi Ghosh. 2024. Applicability of large language models and generative models for legal case judgement summarization. Artificial Intelligence and Law, pages 1--44

    2024

  11. [19]

    Quang Do, Wei Lu, and Dan Roth. 2012. Joint inference for event timeline construction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 677--687

    2012

  12. [20]

    Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018. Banditsum: Extractive summarization as a contextual bandit. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3739--3748

    2018

  13. [21]

    Mohamed Elaraby and Diane Litman. 2022. Arglegalsumm: Improving abstractive summarization of legal documents with argument mining. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6187--6194

    2022

  14. [22]

    Mohamed Elaraby, Yang Zhong, and Diane Litman. 2023. Towards argument-aware abstractive summarization of long legal opinions with summary reranking. arXiv preprint arXiv:2306.00672

    2023 arXiv

  15. [23]

    G \"u nes Erkan and Dragomir R Radev. 2004. Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research, 22:457--479

    2004

  16. [24]

    Biaoyan Fang, Trevor Cohn, Timothy Baldwin, and Lea Frermann. 2023. Super-scotus: A multi-sourced dataset for the supreme court of the us. In Proceedings of the Natural Legal Language Processing Workshop 2023, pages 202--214

    2023

  17. [25]

    Atefeh Farzindar. 2004. Atefeh farzindar and guy lapalme,'letsum, an automatic legal text summarizing system'in t. gordon (ed.), legal knowledge and information systems. jurix 2004: The seventeenth annual conference. amsterdam: Ios press, 2004, pp. 11-18. In Legal knowledge an...

    2004

  18. [26]

    Diego de Vargas Feijo and Viviane P Moreira. 2023. Improving abstractive summarization of legal rulings through textual entailment. Artificial intelligence and law, 31(1):91--113

    2023

  19. [27]

    Yi Fung, Christopher Thomas, Revanth Gangi Reddy, Sandeep Polisetty, Heng Ji, Shih-Fu Chang, Kathleen McKeown, Mohit Bansal, and Avirup Sil. 2021. Infosurgeon: Cross-media fine-grained information consistency checking for fake news detection. In Proceedings of the 59th Annual ...

    2021

  20. [28]

    Yihong Gong and Xin Liu. 2001. Generic text summarization using relevance measure and latent semantic analysis. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 19--25

    2001

  21. [29]

    Claire Grover, Ben Hachey, Ian Hughson, and Chris Korycinski. 2003 a . Automatic summarisation of legal documents. In Proceedings of the 9th international conference on Artificial intelligence and law, pages 243--251

    2003

  22. [30]

    Claire Grover, Ben Hachey, and Chris Korycinski. 2003 b . Summarising legal texts: Sentential tense and argumentative roles. In Proceedings of the HLT-NAACL 03 Text Summarization Workshop, pages 33--40

    2003

  23. [31]

    Nianlong Gu, Elliott Ash, and Richard Hahnloser. 2022. Memsum: Extractive summarization of long documents using multi-step episodic markov decision processes. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pa...

    2022

  24. [32]

    Markus Guhe. 2020. Incremental conceptualization for language production. Psychology Press

    2020

  25. [33]

    Mandy Guo, Joshua Ainslie, David C Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, and Yinfei Yang. 2022. Longt5: Efficient text-to-text transformer for long sequences. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 724--736

    2022

  26. [34]

    Ben Hachey and Claire Grover. 2006. Extractive summarisation of legal texts. Artificial Intelligence and Law, 14:305--345

    2006

  27. [35]

    Eduard H Hovy. 1993. Automated discourse generation using discourse structure relations. Artificial intelligence, 63(1-2):341--385

    1993

  28. [36]

    Fantine Huot, Joshua Maynez, Chris Alberti, Reinald Kim Amplayo, Priyanka Agrawal, Constanza Fierro, Shashi Narayan, and Mirella Lapata. 2024. µ-plan: Summarizing using a content plan as a cross-lingual bridge. In Proceedings of the 18th Conference of the European Chapter of t...

    2024

  29. [37]

    Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo, Kuzman Ganchev, Annie Priyadarshini Louis, Anders Sandholm, Dipanjan Das, and Mirella Lapata. 2023. Text-blueprint: An interactive platform for plan-based conditional generation. In Proceedings of the 17th Confe...

    2023

  30. [38]

    Maor Ivgi, Uri Shaham, and Jonathan Berant. 2023. Efficient long-text understanding with short-text models. Transactions of the Association for Computational Linguistics, 11:284--299

    2023

  31. [39]

    Bram Jans, Steven Bethard, Ivan Vulic, and Marie-Francine Moens. 2012. Skip n-grams and ranking functions for predicting script events. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2012), pages 336--344. A...

    2012

  32. [40]

    Mohamad Yaser Jaradeh, Kuldeep Singh, Markus Stocker, Andreas Both, and S \"o ren Auer. 2021. Better call the plumber: orchestrating dynamic information extraction pipelines. In International Conference on Web Engineering, pages 240--254. Springer

    2021

  33. [41]

    Abhinav Joshi, Akshat Sharma, Sai Kiran Tanikella, and Ashutosh Modi. 2023. U-creat: Unsupervised case retrieval using events extraction. arXiv preprint arXiv:2307.05260

    2023 arXiv

  34. [42]

    Nikiforos Karamanis. 2004. Entity coherence for descriptive text structuring

    2004

  35. [43]

    Rodger Kibble and Richard Power. 2004. Optimizing referential coherence in text generation. Computational Linguistics, 30(4):401--416

    2004

  36. [44]

    Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

    2014 arXiv

  37. [45]

    Anirban Laha, Parag Jain, Abhijit Mishra, and Karthik Sankaranarayanan. 2020. Scalable micro-planned generation of discourse from structured data. Computational Linguistics, 45(4):737--763

    2020

  38. [46]

    Willem JM Levelt. 1993. Speaking: From intention to articulation. MIT press

    1993

  39. [47]

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58t...

    2020

  40. [48]

    Qingquan Li, Qifan Zhang, Junjie Yao, and Yingjie Zhang. 2020. Event extraction for criminal legal text. In 2020 IEEE international conference on knowledge graph (ICKG), pages 573--580. IEEE

    2020

  41. [49]

    Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74--81

    2004

  42. [50]

    Chao-Lin Liu and Kuan-Chun Chen. 2019. Extracting the gist of chinese judgments of the supreme court. In proceedings of the seventeenth international conference on artificial intelligence and law, pages 73--82

    2019

  43. [51]

    Zhengyuan Liu and Nancy F Chen. 2021. Controllable neural dialogue summarization with personal named entity planning. arXiv preprint arXiv:2109.13070

    2021 arXiv

  44. [52]

    Hans Peter Luhn. 1958. The automatic creation of literature abstracts. IBM Journal of research and development, 2(2):159--165

    1958

  45. [53]

    Mandal, Paheli Bhattacharya, Sekhar Mandal, and Saptarshi Ghosh

    A. Mandal, Paheli Bhattacharya, Sekhar Mandal, and Saptarshi Ghosh. 2021. https://api.semanticscholar.org/CorpusID:245036575 Improving legal case summarization using document-specific catchphrases . In International Conference on Legal Knowledge and Information Systems

    2021

  46. [54]

    William C Mann and Sandra A Thompson. 1988. Rhetorical structure theory: Toward a functional theory of text organization. Text-interdisciplinary Journal for the Study of Discourse, 8(3):243--281

    1988

  47. [55]

    Kathleen McKeown and Dragomir R Radev. 1995. Generating summaries of multiple news articles. In Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pages 74--82

    1995

  48. [56]

    Kathleen R McKeown. 1985. Discourse strategies for generating natural-language text. Artificial intelligence, 27(1):1--41

    1985

  49. [57]

    Chris Mellish, Alistair Knott, Jon Oberlander, and Mick O'Donnell. 1998. Experiments using stochastic search for text planning. In Proceedings of the 9th International General Workshop, pages 98--107. ACL Anthology

    1998

  50. [58]

    Derek Miller. 2019. Leveraging bert for extractive text summarization on lectures. arXiv preprint arXiv:1906.04165

    2019 arXiv

  51. [59]

    Gianluca Moro and Luca Ragazzi. 2022. Semantic self-segmentation for abstractive summarization of long documents in low-resource regimes. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 11085--11093

    2022

  52. [60]

    Amit Moryossef, Yoav Goldberg, and Ido Dagan. 2019. Step-by-step: Separating planning from realization in neural data-to-text generation. arXiv preprint arXiv:1904.03396

    2019 arXiv

  53. [61]

    Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018. Ranking sentences for extractive summarization with reinforcement learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies...

    2018

  54. [62]

    Shashi Narayan, Joshua Maynez, Jakub Adamek, Daniele Pighin, Blaz Bratanic, and Ryan McDonald. 2020. Stepwise extractive summarization and planning with structured transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), ...

    2020

  55. [63]

    Shashi Narayan, Joshua Maynez, Reinald Kim Amplayo, Kuzman Ganchev, Annie Louis, Fantine Huot, Anders Sandholm, Dipanjan Das, and Mirella Lapata. 2023. Conditional generation with a question-answering blueprint. Transactions of the Association for Computational Linguistics, 11...

    2023

  56. [64]

    Shashi Narayan, Yao Zhao, Joshua Maynez, Gon c alo Sim \ o es, Vitaly Nikolaev, and Ryan McDonald. 2021. Planning with learned entity prompts for abstractive summarization. Transactions of the Association for Computational Linguistics, 9:1475--1492

    2021

  57. [65]

    Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532--1543

    2014

  58. [66]

    Laura Perez-Beltrachini and Mirella Lapata. 2018. Bootstrapping generators from noisy data. arXiv preprint arXiv:1804.06385

    2018 arXiv

  59. [67]

    Seth Polsley, Pooja Jhunjhunwala, and Ruihong Huang. 2016. Casesummarizer: a system for automated summarization of legal texts. In Proceedings of COLING 2016, the 26th international conference on Computational Linguistics: System Demonstrations, pages 258--262

    2016

  60. [68]

    Ratish Puduppully, Li Dong, and Mirella Lapata. 2019. Data-to-text generation with entity modeling. arXiv preprint arXiv:1906.03221

    2019 arXiv

  61. [69]

    Ratish Puduppully, Yao Fu, and Mirella Lapata. 2022. Data-to-text generation with variational sequential planning. Transactions of the Association for Computational Linguistics, 10:697--715

    2022

  62. [70]

    Ratish Puduppully and Mirella Lapata. 2021. Data-to-text generation with macro planning. Transactions of the Association for Computational Linguistics, 9:510--527

    2021

  63. [71]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485--5551

    2020

  64. [72]

    Hannah Rashkin, Maarten Sap, Emily Allaway, Noah A Smith, and Yejin Choi. 2018. Event2mind: Commonsense inference on events, intents, and reactions. arXiv preprint arXiv:1805.06939

    2018 arXiv

  65. [73]

    Oliver Ray, Amy Conroy, and Rozano Imansyah. 2020. Summarisation with majority opinion. In JURIX, pages 247--250

    2020

  66. [74]

    Ehud Reiter and Robert Dale. 1997. Building applied natural language generation systems. Natural Language Engineering, 3(1):57--87

    1997

  67. [75]

    TYS Santosh, Vatsal Venkatkrishna, Saptarshi Ghosh, and Matthias Grabmair. 2024 a . Beyond borders: Investigating cross-jurisdiction transfer in legal case summarization. arXiv preprint arXiv:2403.19317

    2024 arXiv

  68. [76]

    TYSS Santosh, Cornelius Weiss, and Matthias Grabmair. 2024 b . Lexsumm and lext5: Benchmarking and modeling legal summarization tasks in english. arXiv preprint arXiv:2410.09527

    2024 arXiv

  69. [77]

    Murali Saravanan, Balaraman Ravindran, and Shivani Raman. 2006. Improving legal document summarization using graphical models. Frontiers in Artificial Intelligence and Applications, 152:51

    2006

  70. [78]

    Marijn Schraagen, Floris Bex, Nick Van De Luijtgaarden, and Dani \"e l Prijs. 2022. Abstractive summarization of dutch court verdicts using sequence-to-sequence models. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 76--87

    2022

  71. [79]

    Shirong Shen, Guilin Qi, Zhen Li, Sheng Bi, and Lusheng Wang. 2020. Hierarchical chinese legal event extraction via pedal attention mechanism. In Proceedings of the 28th international conference on computational linguistics, pages 100--113

    2020

  72. [80]

    Zejiang Shen, Kyle Lo, Lauren Yu, Nathan Dahlberg, Margo Schlanger, and Doug Downey. 2022. Multi-lexsum: Real-world summaries of civil rights lawsuits at multiple granularities. Advances in Neural Information Processing Systems, 35:13158--13173

    2022

  73. [81]

    Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, and Saptarshi Ghosh. 2022. Legal case document summarization: Extractive and abstractive methods and their evaluation. In Proceedings of the 2nd Conference of the Asia-Pacific C...

    2022

  74. [82]

    Santosh Tyss, Mahmoud Aly, and Matthias Grabmair. 2024. Lexabsumm: Aspect-based summarization of legal decisions. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10422--10431

    2024

  75. [83]

    Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8:229--256

    1992

  76. [84]

    Wen Xiao, Iz Beltagy, Giuseppe Carenini, and Arman Cohan. 2022. Primera: Pyramid-based masked sentence pre-training for multi-document summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5245--5263

    2022

  77. [85]

    Huihui Xu and Kevin Ashley. 2022. Multi-granularity argument mining in legal texts. arXiv preprint arXiv:2210.09472

    2022 arXiv

  78. [86]

    Huihui Xu, Jaromir Savelka, and Kevin D Ashley. 2021 a . Accounting for sentence position and legal domain sentence embedding in learning to classify case sentences. In Legal Knowledge and Information Systems, pages 33--42. IOS Press

    2021

  79. [87]

    Huihui Xu, Jaromir Savelka, and Kevin D Ashley. 2021 b . Toward summarizing case decisions via extracting argument issues, reasons, and conclusions. In Proceedings of the eighteenth international conference on artificial intelligence and law, pages 250--254

    2021

  80. [88]

    Hui Yang, Tat-Seng Chua, Shuguang Wang, and Chun-Keat Koh. 2003. Structured use of external knowledge for event-based open domain question answering. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 33--40

    2003

  81. [89]

    Feng Yao, Chaojun Xiao, Xiaozhi Wang, Zhiyuan Liu, Lei Hou, Cunchao Tu, Juanzi Li, Yun Liu, Weixing Shen, and Maosong Sun. 2022. Leven: A large-scale chinese legal event detection dataset. arXiv preprint arXiv:2203.08556

    2022 arXiv

  82. [90]

    Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu. 2023. Alignscore: Evaluating factual consistency with a unified alignment function. arXiv preprint arXiv:2305.16739

    2023 arXiv

  83. [91]

    Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In International Conference on Machine Learning, pages 11328--11339. PMLR

    2020

  84. [92]

    Shiyue Zhang, David Wan, and Mohit Bansal. 2022. Extractive is not faithful: An investigation of broad unfaithfulness problems in extractive summarization. arXiv preprint arXiv:2209.03549

    2022 arXiv

  85. [93]

    Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019 a . Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations

    2019

  86. [94]

    Xingxing Zhang, Furu Wei, and Ming Zhou. 2019 b . Hibert: Document level pre-training of hierarchical bidirectional transformers for document summarization. arXiv preprint arXiv:1905.06566

    2019 arXiv

  87. [95]

    Linwu Zhong, Ziyi Zhong, Zinian Zhao, Siyuan Wang, Kevin D Ashley, and Matthias Grabmair. 2019. Automatic summarization of legal decisions using iterative masking of predictive sentences. In Proceedings of the seventeenth international conference on artificial intelligence and...

    2019

  88. [96]

    Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, and Jiawei Han. 2022. Towards a unified multi-dimensional evaluator for text generation. arXiv preprint arXiv:2210.07197

    2022 arXiv

  89. [97]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  90. [98]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.