Abstract
In the context of text summarization, texts in the legal domain have peculiarities related to their length and to their specialized vocabulary. Recent neural network-based approaches can achieve high-quality scores for text summarization. However, these approaches have been used mostly for generating very short abstracts for news articles. Thus, their applicability to the legal domain remains an open issue. In this work, we experimented with ten extractive and four abstractive models in a real dataset of legal rulings. These models were compared with an extractive baseline based on heuristics to select the most relevant parts of the text. Our results show that abstractive approaches significantly outperform extractive methods in terms of ROUGE scores.- Anthology ID:
- R19-1036
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 313–322
- Language:
- URL:
- https://aclanthology.org/R19-1036
- DOI:
- 10.26615/978-954-452-056-4_036
- Cite (ACL):
- Diego Feijo and Viviane Moreira. 2019. Summarizing Legal Rulings: Comparative Experiments. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 313–322, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Summarizing Legal Rulings: Comparative Experiments (Feijo & Moreira, RANLP 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/R19-1036.pdf