Legal Summarization

This article focused on the comparison of summary algorithms in Indian court decisions. Because of the vast amount of legal information available on the Internet as well as in other sources, it is important for the research community to conduct more in-depth research in the area of legal word processing, which can help us understand the enormous amount of data available. This growth in information has forced the need to develop systems that can help legal professionals and ordinary citizens obtain relevant legal information with very little effort. This discussion paper explores various text summarization techniques, with a particular focus on aggregation of legal documents, as this is one of the most important areas in the legal field that can contribute to the rapid understanding of legal documents. This article begins with the general introduction to the text summary, after which various legal text summary techniques are discussed. Various available tools are also described in this document, which is used to summarize legal texts. This article also presents two case studies on the automatic aggregation of heterogeneous legal documents from two countries. With the detailed review presented of the state of the art of the approaches, the comparative analysis of the case studies, and discussions on several important research questions, it is expected that this work will provide a good starting point for researchers to conduct a more in-depth examination of the field of legal document synthesis, particularly with respect to the key future research directions identified in this work. Steigerung der Produktivität bei der Arbeit durch die Implementierung von KI im Rechtssektor. Testen Sie Legalmind AI Summarizer noch heute. Gelbart D, Smith J (1991) Beyond boolean search: Flexicon, a legal text-based intelligent system. In: proceedings of the 3rd International Conference on Artificial Intelligence and Law, pp 225-234 Zhong L, Zhong Z, Zhao Z, Wang S, Ashley KD, Grabmair M (2019) Automatic summary of legal decisions using iterative masking of predictive phrases. In: proceedings of the seventeenth international conference on artificial intelligence and law, ICAIL `19, S.

163-172 Feijo D, Moreira V (2019) Summarizing legal rulings: comparative experiments. in: Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pp 313-322 Liu Y (2019) Fine-tune BERT for extractive summarization. arXiv Preprint arXiv:190310318 Fan A, Grangier D, Auli M (2018) Controllable abstract summary. In: Proceedings of the 2nd workshop on translation and generation of neural machines, 45–54 The PyldaVis library was used to visualize thematic models. Notice how closely related topics 1 and 4 are and how far apart topics 2, 3, and 5 are. These topics (2, 3 and 5) capture relatively different topics in the legal document and are the ones that should be observed in a more complicated way as they would provide a broader view of the document if combined: Zhao Z, Cohen SB, Webber B (2020) Reduction of Quantity Hallucinations in Abstract Summary. arXiv Preprint arXiv:200913312 Zhang X, Wei F, Zhou M (2019) HIBERT: Document-level pre-training of hierarchical bidirectional transformers for document synthesis. In: proceedings of the 57th annual meeting of the association for computational linguistics, pp 5059-5069 Maynez J, Narayan S, Bohnet B, McDonald R (2020) On faithfulness and factuality in abstractive summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp 1906-1919 Eine praktischere Anwendung dieses Projekts ist die Textzusammenfassung von Kapiteln von Romanen, Lehrbüchern usw., von denen es sich als richtig erwiesen hat. Feijo D, Moreira V (2018) Rulingbr: Ein zusammenfassender Datensatz für Rechtstexte. In: Computer processing of the Portuguese language (PROPOR 2018), Springer International Publishing, S.

255-264 Pandya V (2019) Automatic summary of legal case texts: a hybrid approach. 5th International Conference on Advances in Computer and Information Technology (ACSTY-2019) Der Standardansatz für die abstrakte Textzusammenfassung ist die Verwendung einer Encoder-Decoder-Architektur. The encoder is responsible for capturing the general meaning of the source code, and the decoder is responsible for creating the final summary of the text. While this approach may create summaries similar to human writing, some may contain unrelated or unfaithful information. This problem is called «hallucination» and is a serious problem in legal texts because lawyers rely on these summaries to support their legal arguments when looking for precedents. Another problem is that legal documents are usually very long and may not be fully transmitted to the encoder. We propose our method, called LegalSumm, to solve these problems by creating different «views» of the source code, training summary templates to generate independent versions of the summaries, and applying the Implication module to evaluate the fidelity of these candidate summaries to the source code. We show that the proposed approach allows for the selection of candidate summaries that improve RED scores in all assessed parameters.

You`ve ever wondered how lawyers deal effectively with a bunch of court filings. How do they circumvent the general context of a legal document to determine what to look for before they finally have to retrieve it? It seems pretty easy until you have a 3000-page document with striking details. This is what motivated this project to automatically model topics from a PDF of legal documents and summarize the most important contexts. The legal agreement between the two parties has been provided as a pdf document. The texts of the PDF document were first extracted using the function below. This function extracts all characters from a PDF document except images (although I can modify this to account for this) using the pdf-miner Python library. The function simply takes the name of the PDF document in the home directory, extracts all the characters from it and displays the extracted texts as a list of Python strings. This repository contains implementations and reproductions of various summary algorithms applied to legal case documents and the article codebase: Grover C, Hachey B, Hughson I, Korycinski C (2003a) Automatic summary of legal documents. In: proceedings of the 9th International Conference on Artificial Intelligence and Law, Association for Computer Machines, ICAIL `03, p 243-251 B integrating topics 2, 3 and 5 received by the Latent Dirichlet Allocation modeling with the word cloud generated for the legal document, we can conclude that this document is a simple legal link between two parties for a trademark domain name transfer. Turtle H (1995) Text retrieval in the legal world. Artif Intell and Law 3(1):5–54 A word cloud was also generated for the entire legal document to note the most common terms in the document, as shown in the following figure.

This is usually consistent with the results of the modeling topic, as words such as: brand, agreement, domain name, eclipse, etc. are considered recurring and therefore bolder. Lin CY (2004) Red: a package for automatic evaluation of abstracts. In: text summarization branchout, pp. 74-81 Cutting-edge approaches to text summarization in general are discussed. Matsumaru K, Takase S, Okazaki N (2020) Text summary with pre-trained encoders. In: proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Joint International Conference on Natural Language Processing (EMNLP-IJCNLP), pp 3721-3731 Yousfi-Monod M, Farzindar A, Lapalme G (2010) Supervised machine learning to summarize legal documents. In: Canadian Conference on Artificial Intelligence, Springer, pp. 51–62 This project takes a simple approach to extracting text from documents to pdf, this project can be modified to obtain text from an image file (.jpeg .png) so that modeling and summarizing topics can be done on a snapshot of documents. The project shows how machine learning can typically be applied in the legal sector to provide a summary of the topic in front of documents. Zhang J, Zhao Y, Saleh M, Liu P (2020) Pegasus: Pre-workout with deviation sentences extracted for abstract summary.

In: International Conference on Machine Learning, PMLR, pp 11328-11339 Feijo, D.d., Moreira, V.P. Improving abstractive summarization of legal rulings through textual involvement. Artif Intell Law (2021). doi.org/10.1007/s10506-021-09305-4 The NLP Summaryr is trained to understand legal jargon and extract relevant information that will help them create a meaningful summary and thus improve efficiency. Cao Z, Wei F, Li W, Li S (2018) True to the original: abstract neuronal summary conscious of the facts. In: AAAI Thirty-second Conference on Artificial Intelligence The summary of the text in the field of law is discussed in detail. Mise en œuvre de divers algorithmes sommaires appliqués aux jugements des tribunaux. Moens MF, Uyttendaele C (1997) Automatic text structuring and categorization as a first step in the summary of legal cases.