Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents
| Autor: | Soham Poddar, Koustav Rudra, Kripabandhu Ghosh, Saptarshi Ghosh, Paheli Bhattacharya |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Information retrieval Computer Science - Computation and Language Computer science 06 humanities and the arts 02 engineering and technology 0603 philosophy ethics and religion Automatic summarization Supreme court Computer Science - Information Retrieval 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing 060301 applied ethics Legal case Computation and Language (cs.CL) Integer programming Information Retrieval (cs.IR) Document summary |
| Zdroj: | ICAIL |
| Popis: | Automatic summarization of legal case documents is an important and practical challenge. Apart from many domain-independent text summarization algorithms that can be used for this purpose, several algorithms have been developed specifically for summarizing legal case documents. However, most of the existing algorithms do not systematically incorporate domain knowledge that specifies what information should ideally be present in a legal case document summary. To address this gap, we propose an unsupervised summarization algorithm DELSumm which is designed to systematically incorporate guidelines from legal experts into an optimization setup. We conduct detailed experiments over case documents from the Indian Supreme Court. The experiments show that our proposed unsupervised method outperforms several strong baselines in terms of ROUGE scores, including both general summarization algorithms and legal-specific ones. In fact, though our proposed algorithm is unsupervised, it outperforms several supervised summarization models that are trained over thousands of document-summary pairs. Accepted at the 18th International Conference on Artificial Intelligence and Law (ICAIL) 2021 |
| Databáze: | OpenAIRE |
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