The automatic analysis of documents is a fundamental aspect of numerous professional and academic practices, including compliance, contract management, litigation support, research, and requirement analysis. These processes typically entail the extraction of crucial information from intricate technical language, the evaluation of potential risks, the assurance of adherence to pertinent rules and standards, and the formulation of recommendations for practical implementation. For example, the analysis of legal documents is fundamental in requirement analysis: the compliance of software requirements must be checked against relevant standards and regulations.
Leveraging LLMs in automatic text analysis and requirements compliance checking enables efficient processing of complex language structures, enhances accuracy in extracting and interpreting critical information, and facilitates adherence to standards by providing intelligent, context-aware insights.
The goal of this Research Topic is to promote and stimulate cutting-edge research exploring new methodologies, models, and applications of automatic text analysis processes employing Large Language Models. This article collection focuses on the use of Natural Language Processing (NLP) and Large Language Models (LLMs) in the processing, interpretation, and analysis of textual materials. Among other document-related activities, this Topic will examine how these sophisticated AI models may be used for automated content summarization, sentiment analysis, entity recognition, topic modeling, and the interpretation of legal or medical documents.
Particular Issues to Discuss:
- The process of automatically classifying and grouping unstructured text into predetermined categories (such as legal documents, scientific publications, and social media postings) using LLMs and NLP algorithms (document classification and categorization).
- Examining the use of LLMs for automatic abstractive and extrovert text summarization across a range of fields, including academic research, corporate reports, and news stories (text summarization).
- Investigating the use of natural language processing (NLP) to extract and classify certain items (such as dates, legal phrases, and medical conditions) from specialized documents: NER in legal/medical and requirements documents.
Manuscript Types of Interest:
- Empirical Studies: Studies showing the efficacy of LLMs and NLP techniques in certain document analysis tasks with quantifiable outcomes.
- Theoretical Papers: These are articles that address novel frameworks, models, or theoretical strategies for enhancing document analysis with LLMs and NLP.
- Case Studies: Practical uses of LLMs in document processing in industries including banking, healthcare, and law.
- Methodological Papers: These are articles that present new methods, systems, or algorithms that improve LLMs' document analyzing skills.
- Reviews of NLP tools, libraries, or platforms created especially for document analysis are referred to as system and tool reviews.
Keywords: Large Language Models (LLMs), Contract Analysis, Text Analysis, Clause Extraction, Compliance Checking, Risk Assesment, Requirement analysis, traditional NLP techniques.
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
