We address the legal text understanding task, and in particular we treat Japanese judgment documents in civil law. Rhetorical status classification (RSC) is the task of classifying sentences according to the rhetorical functions they fulfil; it is an important preprocessing step for our overall goal of legal summarisation. We present several improvements over our previous RSC classifier, which was based on CRF. The first is a BiLSTM-CRF based model which improves performance significantly over previous baselines. The BiLSTM-CRF architecture is able to additionally take the context in terms of neighbouring sentences into account. The second improvement is the inclusion of section heading information, which resulted in the overall best classifier. Explicit structure in the text, such as headings, is an information source which is likely to be important to legal professionals during the reading phase; this makes the automatic exploitation of such information attractive. We also considerably extended the size of our annotated corpus of judgment documents.
Hiroaki Yamada, Simone Teufel and Takenobu Tokunaga. 2019. Neural network based Rhetorical status classification for Japanese judgement documents. In The proceedings of the 32nd International Conference on Legal Knowledge and Information Systems (JURIX 2019). pages 133–142.