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Circumstances enhanced Criminal Court View Generation

Published: 11 July 2021 Publication History

Abstract

Criminal Court View Generation is an essential task in legal intelligence, which aims to automatically generate sentences interpreting judgment results. The court view could be seen as the summary of crime circumstances in a case, including ADjudging Circumstance (ADC) and SEntencing Circumstance (SEC). However, different circumstances vary widely, and adopting them to generate court views directly may limit the generation performance. Therefore, it is necessary to identify the ADC and SEC related sentences in case facts and enhance them into the court view generation, respectively. To this end, in this paper, we propose a novel Circumstances enhanced Criminal Court View Generation (C3VG) method, consisting of the extraction and generation stage. Specifically, in the extraction stage, we design a Circumstances Selector to select ADC and SEC related sentences. After that, we apply them to two generators to generate the circumstances enhanced court views, respectively. After merging the two types of court views, we could obtain the final court views. We evaluate C3VG by conducting extensive experiments on a real-world dataset and experimental results clearly validate the effectiveness of our proposed model.

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Criminal Court View Generation is an essential task in legal intelligence, which aims to automatically generate sentences interpreting judgment results. The court view could be seen as the summary of crime circumstances in a case, including ADjudging Circumstance (ADC) & SEntencing Circumstance (SEC). However, different circumstances vary widely, & adopting them to generate court views directly may limit the generation performance. Therefore, it is necessary to identify the ADC & SEC related sentences in case facts & enhance them into the court view generation, respectively. To this end, in this paper, we propose a novel Circumstances enhanced Criminal Court View Generation (C3VG) method, consisting of the extraction & generation stage. Specifically, in the extraction stage, we design a Circumstances Selector to select ADC & SEC related sentences.

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Cited By

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  • (2024)Event Grounded Criminal Court View Generation with Cooperative (Large) Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657698(2221-2230)Online publication date: 10-Jul-2024
  • (2024)DuaPIN: Auxiliary task enhanced dual path interaction network for civil court view generationKnowledge-Based Systems10.1016/j.knosys.2024.111728295(111728)Online publication date: Jul-2024
  • (2023)Explaining legal judgments: A multitask learning framework for enhancing factual consistency in rationale generationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10186835:10(101868)Online publication date: Dec-2023

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    cover image ACM Conferences
    SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2021
    2998 pages
    ISBN:9781450380379
    DOI:10.1145/3404835
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 11 July 2021

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    Author Tags

    1. court views
    2. crime circumstances
    3. text generation

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    View all
    • (2024)Event Grounded Criminal Court View Generation with Cooperative (Large) Language ModelsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657698(2221-2230)Online publication date: 10-Jul-2024
    • (2024)DuaPIN: Auxiliary task enhanced dual path interaction network for civil court view generationKnowledge-Based Systems10.1016/j.knosys.2024.111728295(111728)Online publication date: Jul-2024
    • (2023)Explaining legal judgments: A multitask learning framework for enhancing factual consistency in rationale generationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10186835:10(101868)Online publication date: Dec-2023

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