Extractive summarisation of legal texts
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Summary
Results are encouraging as they achieve state-of-the-art accuracy using robust, automatically generated cue phrase information and the utility of the rhetorical annotation scheme as a model of legal discourse, which provides a clear means for structuring summaries and tailoring them to different types of users.
- Type
- article
- Published
- 2006-12-01
- Cited by
- 155
- References
- 71
- Access
- Open access
- OpenAlex
- https://openalex.org/W2120879767
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10295764
Keywords
Rhetorical question, Computer science, Task (project management), Natural language processing, Structuring
References
- A Preliminary Study of Lexical Density for the Development of XML-based Discourse Structure Tagger
- Résumé automatique de textes juridiques
- Genre Analysis: English in Academic and Research Settings
- Automatic Rule Induction for Unknown-Word Guessing
- Teaching case-based argumentation through a model and examples
- Abstracting Concepts and Methods
- Advances in Automatic Text Summarization
- Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
- Advances in kernel methods: support vector learning
- Introduction to the CoNLL-2001 shared task: clause identification
- A Maximum Entropy Model for Part-Of-Speech Tagging
- The PageRank Citation Ranking : Bringing Order to the Web
- Maximum Entropy Markov Models for Information Extraction and Segmentation
- Using maximum entropy for sentence extraction
- The automatic construction of large-scale corpora for summarization research
- Automatic summarisation of legal documents
- Generalized Iterative Scaling for Log-Linear Models
- Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger
- Language Independent NER using a Maximum Entropy Tagger
- Robust, applied morphological generation
Cited by
- Combining Different Summarization Techniques for Legal Text
- Towards Annotating and Extracting Textual Legal Case Factors
- Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints
- Concept relation extraction using natural language processing: the CRISP technique
- Singling out Legal Knowledge from World Knowledge. An NLP-based approach
- Towards Annotating and Extracting Textual Legal Case Elements
- Sentence simplification, compression, and disaggregation for summarization of sophisticated documents
- LEXA: Building knowledge bases for automatic legal citation classification
- Summarization based on bi-directional citation analysis
- HAUSS: Incrementally building a summarizer combining multiple techniques
- Query-based opinion summarization for legal blog entries
- Introducing LUIMA: an experiment in legal conceptual retrieval of vaccine injury decisions using a UIMA type system and tools
- Automatically classifying case texts and predicting outcomes
- Improving the comprehension of legal documentation: the case of patent claims
- Identification of Rhetorical Roles for Segmentation and Summarization of a Legal Judgment
- Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings
- Weakly supervised learning of information structure of scientific abstracts - is it accurate enough to benefit real-world tasks in biomedicine?
- A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents
- Extracting information from fiction
- Document and Corpus Level Inference For Unsupervised and Transductive Learning of Information Structure of Scientific Documents
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