Incorporating domain knowledge for extractive summarization of legal case documents

Bhattacharya, Paheli; Poddar, Soham; Rudra, Koustav; Ghosh, Kripabandhu; Ghosh, Saptarshi

doi:10.1145/3462757.3466092

Cited by 42 publications

(11 citation statements)

References 10 publications

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“…The foundational paper "ILDC for CJPE" [10] inspired this extended study, supported by related works like "DELSumm [1], " "Identification of Rhetorical Roles of Sentences in Indian Legal Judgments [4], " and the research on semantic segmentation of legal documents [9]. These works provide a framework for our methodological design.…”

Section: Related Workmentioning

confidence: 90%

Fact-based Court Judgment Prediction

Nigam,

Deroy

2023

Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation

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This extended abstract extends the research presented in "ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation" [10], focusing on fact-based judgment prediction within the context of Indian legal documents. We introduce two distinct problem variations: one based solely on facts, and another combining facts with rulings from lower courts (RLC). Our research aims to enhance early-phase case outcome prediction, offering significant benefits to legal professionals and the general public. The results, however, indicated a performance decline compared to the original ILDC for CJPE study, even after implementing various weightage schemes in our DELSumm algorithm. Additionally, using only facts for legal judgment prediction with different transformer models yielded results inferior to the state-of-the-art outcomes reported in the "ILDC for CJPE" study.

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Section: Related Workmentioning

confidence: 90%

Fact-based Court Judgment Prediction

Nigam,

Deroy

2023

Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation

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“…Such pipelines rely not only on the content of the sentence itself but also on its rhetorical role [7] (i.e. whether the sentence belongs to the Fact, the Issue or the Conclusion section of the case) whose importance was highlighted by [8], [9] and [10] for British, Taiwanese and Indian courts cases, respectively.…”

Section: Related Workmentioning

confidence: 99%

Conditional Abstractive Summarization of Court Decisions for Laymen and Insights from Human Evaluation

Salaün

Troussel

Longhais

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Legal text summarization is generally formalized as an extractive text summarization task applied to court decisions from which the most relevant sentences are identified and returned as a gist meant to be read by legal experts. However, such summaries are not suitable for laymen seeking intelligible legal information. In the scope of the JusticeBot, a question-answering system in French that provides information about housing law, we intend to generate summaries of court decisions that are, on the one hand, conditioned by a question-answer-decision triplet, and on the other hand, intelligible for ordinary citizens not familiar with legal documents. So far, our best model, a further pre-trained BARThez, achieves an average ROUGE-1 score of 37.7 and a deepened manual evaluation of summaries reveals that there is still room for improvement.

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“…Instead, prior work has tackled legal extractive summarization by applying domain independent unsupervised algorithms (Luhn, 1958;Erkan and Radev, 2004;Saravanan et al, 2006), or designing domain specific supervised approaches (Saravanan et al, 2006;Polsley et al, 2016;Zhong et al, 2019). One recent work (Bhattacharya et al, 2021) frames the task as Integer Linear Programming and demonstrates the importance of in-domain structure and legal knowledge. In another line of research, Xu et al (2021) propose a sentence classification task with the hope of exploiting the court decision's argument structure by making explicit its issues, conclusions, and reasons (i.e., argument triples).…”

Section: Related Workmentioning

confidence: 99%

Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions

Zhong¹,

Litman²

2022

Preprint

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Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case summarization dataset demonstrates that sentences serving different types of argumentative roles in the decision appear in different sections of the document. In this work, we propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit properties of the document structure of legal case decisions. We also explore the impact of using different methods to compute the document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.

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Incorporating domain knowledge for extractive summarization of legal case documents

Cited by 42 publications

References 10 publications

Fact-based Court Judgment Prediction

Fact-based Court Judgment Prediction

Conditional Abstractive Summarization of Court Decisions for Laymen and Insights from Human Evaluation

Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions

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