Automatic Catchphrase Identification from Legal Court Case Documents

Bhattacharya

Frontiers in Artificial Intelligence and Applications

et al. 2021

Self Cite

Legal case summarization is an important problem, and several domain-specific summarization algorithms have been applied for this task. These algorithms generally use domain-specific legal dictionaries to estimate the importance of sentences. However, none of the popular summarization algorithms use document-specific catchphrases, which provide a unique amalgamation of domain-specific and document-specific information. In this work, we assess the performance of two legal document summarization algorithms, when two different types of catchphrases are incorporated in the summarization process. Our experiments confirm that both the summarization algorithms show improvement across all performance metrics, with the incorporation of document-specific catchphrases.

“…Step 1: Some candidate phrases are extracted from d. These are actually noun phrases extracted using a customized set of grammatical rules (details in [9]).…”

Section: Pslegal [9] -An Unsupervised Methodmentioning

confidence: 99%

Section: Legal Catchphrase Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Legal Case Summarization Using Document-Specific Catchphrases

Bhattacharya

Frontiers in Artificial Intelligence and Applications

et al. 2021

Self Cite

Frontiers in Artificial Intelligence and Applications

“…Characterizing the length and legal keywords content of frequently selected/missed sentences: We found that frequently missed sentences have a similar distribution of length (number of words) as frequently selected sentences. We checked the number of legal keywords contained in the two types of sentences, using terms from a legal dictionary provided by [18]. The frequently selected sentences contain 3.30 legal terms on average, while frequently missed sentences contain 2.89 legal terms on average.…”

Section: Number Of Algorithmsmentioning

confidence: 99%

An Analytical Study of Algorithmic and Expert Summaries of Legal Cases

Deroy

Bhattacharya

Ghosh

et al. 2021

Self Cite

Automatic summarization of legal case documents is an important and challenging problem, where algorithms attempt to generate summaries that match well with expert-generated summaries. This work takes the first step in analyzing expert-generated summaries and algorithmic summaries of legal case documents. We try to uncover how law experts write summaries for a legal document, how various generic as well as domain-specific extractive algorithms generate summaries, and how the expert summaries vary from the algorithmic summaries. We also analyze which important sentences of a legal case document are missed by most algorithms while generating summaries, in terms of the rhetorical roles of the sentences and the positions of the sentences in the legal document.

“…Legal information retrieval systems require to identify catchphrases from judgments automatically, a mechanism needs to be explored in depth. Mandal [41] proposed an approach using unsupervised learning, for extraction and ranking of catchphrases automatically using the noun phrases from judgments. The proposed system is compared with different supervised and unsupervised baseline systems and getting statistically better performance over those baseline systems.…”

Section: Proposed Approaches For Indian Legal Systemmentioning

confidence: 99%

An overview of information extraction techniques for legal document analysis and processing

Zadgaonkar¹,

Agrawal²

2021

IJECE

<span>In an Indian law system, different courts publish their legal proceedings every month for future reference of legal experts and common people. Extensive manual labor and time are required to analyze and process the information stored in these lengthy complex legal documents. Automatic legal document processing is the solution to overcome drawbacks of manual processing and will be very helpful to the common man for a better understanding of a legal domain. In this paper, we are exploring the recent advances in the field of legal text processing and provide a comparative analysis of approaches used for it. In this work, we have divided the approaches into three classes NLP based, deep learning-based and, KBP based approaches. We have put special emphasis on the KBP approach as we strongly believe that this approach can handle the complexities of the legal domain well. We finally discuss some of the possible future research directions for legal document analysis and processing.</span>