LegalOps: A Summarization Corpus of Legal Opinions

Gargett, Andrew; Firth, Rob and Aletras, Nikolaos (2020). LegalOps: A Summarization Corpus of Legal Opinions. In: 2020 IEEE International Conference on Big Data (Big Data), IEEE, pp. 2117–2120.

DOI: https://doi.org/10.1109/BigData50022.2020.9378308

Abstract

We present a new, large-scale corpus for training and evaluating text summarization systems on legal opinions, called LegalOps. The corpus includes ~ 14K opinions together with their summaries from U.S. Federal Courts, e.g. the Supreme Court and Federal Appeals Courts. The aim of this paper is to provide a novel data source of sufficient variety that it will advance theoretical work on modeling the particular patterns within legal discourse, but also of sufficient size that it will provide a new challenging testbed for state-of-the-art automatic summarization models.

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• Item ORO ID
• 75800
• Item Type
• Conference or Workshop Item
• ISBN
• 978-1-7281-6251-5/20
• Academic Unit or School
• Faculty of Wellbeing, Education and Language Studies (WELS) > Languages and Applied Linguistics > Languages
  Faculty of Wellbeing, Education and Language Studies (WELS) > Languages and Applied Linguistics
  Faculty of Wellbeing, Education and Language Studies (WELS)
• Copyright Holders
• © 2020 IEEE
• Depositing User
• Andrew Gargett