Text summarization from legal documents: a survey

Kanapala, Ambedkar; Pal, Sukomal; Pamula, Rajendra

doi:10.1007/s10462-017-9566-2

Cited by 126 publications

(49 citation statements)

References 49 publications

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“…NLP has been used to automatically extract and classify relevant entities in court documents [13,14,4]. Other works [15,16,17,18] focus on using automatic summarization to reduce the amount of information legal professionals have to process. Document classification has been explored for decision prediction [19,20], area of legal practice attribution [21] and fine-grained legal-issue classification [22].…”

Section: Natural Language Processing and Topic Models In Legal Textmentioning

confidence: 99%

Topic Modelling Brazilian Supreme Court Lawsuits

Araujo

Campos

2020

Frontiers in Artificial Intelligence and Applications

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The present work proposes the use of Latent Dirichlet Allocation to model Extraordinary Appeals received by Brazil’s Supreme Court. The data consist of a corpus of 45,532 lawsuits manually annotated by the Court’s experts with theme labels, a multi-class and multi-label classification task. We initially train models with 10 and 30 topics and analyze their semantics by examining each topic’s most relevant words and their most representative texts, aiming to evaluate model interpretability and quality. We also train models with 30, 100, 300 and 1,000 topics, and quantitatively evaluate their potential using the topics to generate feature vectors for each appeal. These vectors are then used to train a lawsuit theme classifier. We compare traditional bag-of-words approaches (word counts and tf-idf values) with the topic-based text representation to assess topic relevancy. Our topics semantic analysis demonstrate that our models with 10 and 30 topics were capable of capturing some of the legal matters discussed by the Court. In addition, our experiments show that the model with 300 topics was the best text vectoriser and that the interpretable, low dimensional representations it generates achieve good classification results.

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Section: Natural Language Processing and Topic Models In Legal Textmentioning

confidence: 99%

Topic Modelling Brazilian Supreme Court Lawsuits

Araujo

Campos

2020

Frontiers in Artificial Intelligence and Applications

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“…Law prediction [1,20] and charge prediction [8,13,25] have been widely studied, especially, CAIL2018 (Chinese AI and Law challenge, 2018) [22,26] was held to predict the judgment results of legal cases including relevant law articles, charges and prison terms. Some other researches include text summarization for legal documents [11], legal consultation [15,24] and legal entity identification [23]. There also exists some systems for similar cases search, legal documents correction and so on.…”

Section: Introductionmentioning

confidence: 99%

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

Duan¹,

Wang²,

Wang³

et al. 2019

Lecture Notes in Computer Science

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We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators.

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“…As demonstrated in the paper, the time complexity of the method is equivalent to that of early graph-based summarization systems such as LexRank [1], which makes it more efficient than existing hypergraph-based summarizers [4,5]. The scalability of summarization algorithms is essential, especially in applications involving large corpora such as the summarization of news reports [8] or the summarization of legal texts [9].…”

Section: Introductionmentioning

confidence: 99%

Query-oriented text summarization based on hypergraph transversals

Lierde

Chow

2019

Information Processing & Management

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Existing graph-and hypergraph-based algorithms for document summarization represent the sentences of a corpus as the nodes of a graph or a hypergraph in which the edges represent relationships of lexical similarities between sentences. Each sentence of the corpus is then scored individually, using popular node ranking algorithms, and a summary is produced by extracting highly scored sentences. This approach fails to select a subset of jointly relevant sentences and it may produce redundant summaries that are missing important topics of the corpus. To alleviate this issue, a new hypergraph-based summarizer is proposed in this paper, in which each node is a sentence and each hyperedge is a theme, namely a group of sentences sharing a topic. Themes are weighted in terms of their prominence in the corpus and their relevance to a user-defined query. It is further shown that the problem of identifying a subset of sentences covering the relevant themes of the corpus is equivalent to that of finding a hypergraph transversal in our theme-based hypergraph. Two extensions of the notion of hypergraph transversal are proposed for the purpose of summarization, and polynomial time algorithms building on the theory of submodular functions are proposed for solving the associated discrete optimization problems. The worst-case time complexity of the proposed algorithms is squared in the number of terms, which makes it cheaper than the existing hypergraph-based methods. A thorough comparative analysis with related models on DUC benchmark datasets demonstrates the effectiveness of our approach, which outperforms existing graph-or hypergraph-based methods by at least

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Text summarization from legal documents: a survey

Cited by 126 publications

References 49 publications

Topic Modelling Brazilian Supreme Court Lawsuits

Topic Modelling Brazilian Supreme Court Lawsuits

CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

Query-oriented text summarization based on hypergraph transversals

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