An Automatic Legal Document Summarization and Search Using Hybrid System
Abstract:
In this paper we propose a hybrid system for automatic text summarization and automatic search task related to legal documents in the legal domain. Manual summarization requires much human effort and time. For this reason automatic text summarization is introduced which saves the legal expert time. The summarization task involves the identification of rhetorical roles presenting the sentences of a legal judgement document. The search task involves the identification of related past cases as per the given legal query. For these two tasks we have introduced hybrid system which is the combination of different techniques. The techniques involved in our hybrid system are keyword or key phrase matching technique and case based technique. We have implemented and tested and required results are produced.
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A Semantic Based Approach for Automatic Patent Document Summarization
Abstract:
With the rapid increase of patent documents, patent analysis becomes an important issue for creating innovative products and reducing time-to-market. A critical task for R&D groups is the analysis of existing patents and the synthesis of existing product knowledge. In order to increase knowledge visibility and sharing among R&D groups, a conconcurrent engineering approach is used to facilitate patent document summarization and sharing. The purpose of this research is to automate patent docment summarization as a key step toward efficient patent analysis and knowledge management. The goal of this research is to develop a semantic-based methodology for capturing patents and to summarize this content for design team collaboration. The system automatically marks, annotates, and highlights the nodes of an ontology tree which correspond to words and provides a visual figure summary. Patents for power hand tools and chemical mechanical polishing tools were downloaded to evaluate the automatic summarization system. For these cases, the accuracy of classification reached 93% and 92% respectively demonstrating 20% summarization improvements over previous methods.
Identification of Rhetorical Roles for Segmentation and Summarization of a Legal Judgment
Abstract:
Legal judgments are complex in nature and hence a brief summary of the judgment, known as a headnote, is generated by experts to enable quick perusal. Headnote generation is a time consuming process and there have been attempts made at automating the process. The difficulty in interpreting such automatically generated summaries is that they are not coherent and do not convey the relative relevance of the various components of the judgment. A legal judgment can be segmented into coherent chunks based on the rhetorical roles played by the sentences. In this paper, a comprehensive system is proposed for labeling sentences with their rhetorical roles and extracting structured head notes automatically from legal judgments. An annotated data set was created with the help of legal experts and used as training data. A machine learning technique, Conditional Random Field, is applied to perform document segmentation by identifying the rhetorical roles. The present work also describes the application of probabilistic models for the extraction of key sentences and composing the relevant chunks in the form of a headnote. The understanding of basic structures and distinct segments is shown to improve the final presentation of the summary. Moreover, by adding simple additional features the system can be extended to other legal sub-domains. The proposed system has been empirically evaluated and found to be highly effective on both the segmentation and summarization tasks. The final summary generated with underlying rhetorical roles improves the readability and efficiency of the system.
An Automatic System for Summarization and Information Extraction of Legal Information
Abstract:
This paper presents an information system for legal professionals that integrates natural language processing technologies such as text classification and summarization. We describe our experience in the use of a mix of linguistics aware transductor and XML technologies for bilingual information extraction from judgements in both French and English within a legal information and summarizing system. We present the context of the work, the main challenges and how they were tackled by clearly separating language and domain dependent terms and vocabularies. After having been developed on the immigration law domain, the system was easily ported to the intellectual property and tax law domains.
Citation Based Summarisation of Legal Texts
Abstract:
This paper presents an approach towards using both incoming and outgoing citation information for document summarisation. Our work aims at generating automatically catchphrases for legal case reports, using, beside the full text, also the text of cited cases and cases that cite the current case. We propose methods to use catchphrases and sentences of cited/citing cases to extract catchphrases from the text of the target case. We created a corpus of cases, catchphrases and citations, and performed a ROUGE based evaluation, which shows the superiority of our citation-based methods over full-text-only methods.
Do we have codes available for any of these papers?