Artificial Intelligence and Law

Springer Netherlands
Springer Journals
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Multi-language transfer learning for low-resource legal case summarization

Moro, Gianluca; Piscaglia, Nicola; Ragazzi, Luca; Italiani, Paolo

2023 Artificial Intelligence and Law

doi: 10.1007/s10506-023-09373-8

Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries.
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