Preface to the Special Issue of the TAL Journal on Scholarly Document Processing

TL;DR

This special issue discusses the challenges of processing scholarly documents and explores how large language models can enhance research tasks like literature reviews and writing assistance.

Contribution

It provides an overview of recent research on NLP and information retrieval techniques tailored for complex scholarly texts and highlights the potential of LLMs in this domain.

Findings

LLMs enable improved literature review automation

Advanced NLP methods address scholarly text complexity

Research highlights new tools for scientific document analysis

Abstract

The rapid growth of scholarly literature makes it increasingly difficult for researchers to keep up with new knowledge. Automated tools are now more essential than ever to help navigate and interpret this vast body of information. Scientific papers pose unique difficulties, with their complex language, specialized terminology, and diverse formats, requiring advanced methods to extract reliable and actionable insights. Large language models (LLMs) offer new opportunities, enabling tasks such as literature reviews, writing assistance, and interactive exploration of research. This special issue of the TAL journal highlights research addressing these challenges and, more broadly, research on natural language processing and information retrieval for scholarly and scientific documents.