In this paper, we propose a machine learning approach to rhetorical role identification from legal documents. In our approach, we annotate roles in sample documents with the help of legal experts and take them as training data. Conditional random field model has been trained with the data to perform rhetorical role identification with reinforcement of rich feature sets. The understanding of structure of a legal document and the application of mathematical model can brings out an effective summary in the final stage. Other important new findings in this work include that the training of a model for one sub-domain can be extended to another sub-domains with very limited augmentation of feature sets. Moreover, we can significantly improve extraction-based summarization results by modifying the ranking of sentences with the importance of specific roles.
In the work of Saravanan, et al. REF the authors propose a mechanical learning approach in order to identify rhetorical roles in legal documents.
Automatic Identification of Rhetorical Roles using Conditional Random Fields for Legal Document Summarization
Proceedings of the Third International Joint Conference on Natural Language Processing
Computer Science