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Gargett, Andrew; Firth, Rob and Aletras, Nikolaos
(2020).
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