Towards Cross-Domain Transferability of Text Generation Models for Legal Text
Vinayshekhar Bannihatti Kumar, Kasturi Bhattacharjee, Rashmi Gangadharaiah
Abstract
Legalese can often be filled with verbose domain-specific jargon which can make it challenging to understand and use for non-experts. Creating succinct summaries of legal documents often makes it easier for user comprehension. However, obtaining labeled data for every domain of legal text is challenging, which makes cross-domain transferability of text generation models for legal text, an important area of research. In this paper, we explore the ability of existing state-of-the-art T5 & BART-based summarization models to transfer across legal domains. We leverage publicly available datasets across four domains for this task, one of which is a new resource for summarizing privacy policies, that we curate and release for academic research. Our experiments demonstrate the low cross-domain transferability of these models, while also highlighting the benefits of combining different domains. Further, we compare the effectiveness of standard metrics for this task and illustrate the vast differences in their performance.- Anthology ID:
- 2022.nllp-1.9
- Volume:
- Proceedings of the Natural Legal Language Processing Workshop 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Nikolaos Aletras, Ilias Chalkidis, Leslie Barrett, Cătălina Goanță, Daniel Preoțiuc-Pietro
- Venue:
- NLLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 111–118
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.nllp-1.9/
- DOI:
- 10.18653/v1/2022.nllp-1.9
- Cite (ACL):
- Vinayshekhar Bannihatti Kumar, Kasturi Bhattacharjee, and Rashmi Gangadharaiah. 2022. Towards Cross-Domain Transferability of Text Generation Models for Legal Text. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 111–118, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Towards Cross-Domain Transferability of Text Generation Models for Legal Text (Bannihatti Kumar et al., NLLP 2022)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2022.nllp-1.9.pdf