LegoNet - classification and extractive summarization of Indian legal judgments with Capsule Networks and Sentence Embeddings

Autor:	Ramamoorthy Srinath, K. Avinash, Aditya D. Bhat, Harshith R. Acharya
Rok vydání:	2020
Předmět:	Statistics and Probability Artificial Intelligence Computer science business.industry General Engineering Artificial intelligence computer.software_genre business Automatic summarization computer Sentence Natural language processing
Zdroj:	Journal of Intelligent & Fuzzy Systems. 39:2037-2046
ISSN:	1875-8967 1064-1246
DOI:	10.3233/jifs-179870
Popis:	In this paper, we propose the LegoNet - a system to classify and summarize legal judgments using Sentence Embedding, Capsule Networks and Unsupervised Extractive Summarization. To train and test the system, we have created a mini-corpus of Indian legal judgments which have been annotated according to the classes: Facts, Arguments, Evidences and Judgments. The proposed framework uses Sentence Embedding and Capsule Networks to classify parts of legal judgments into the classes mentioned above. This is then used by the extractive summarizer to generate a concise and succinct summary of the document grouped according to the above mentioned classes. Such a system could be used to help enable the Legal Community by speeding up the processes involving reading and summarizing legal documents which a Law professional would undertake in preparing for a case. The performance of the Machine Learning Model in this architecture can improve over time as more annotated training data is added to the corpus.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::312073991ef0d20efc9f2599f8267a16 https://doi.org/10.3233/jifs-179870 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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