Natural Language Generation

2018-10-24

Theoretical Issues In Natural Language Processing

AI Summary

Natural language generation (NLG) lacks a unified conceptual framework and clearly delineated disciplinary boundaries, leading to ambiguity in its scope relative to other NLP subfields such as machine translation and dialogue systems. Method: This paper systematically surveys NLG’s research landscape and historical evolution, focusing on core tasks—including data-to-text generation, text summarization, and image captioning—and proposes a novel taxonomy grounded in the dual primitives of “content selection” and “realization.” It traces methodological shifts from rule-based and statistical approaches to early neural models, analyzing how task-driven evaluation paradigms have evolved. Contribution/Results: The work establishes NLG’s formal disciplinary boundaries for the first time, constructs a widely cited conceptual framework and discipline map, clarifies terminological consensus, and identifies large language models as catalysts for methodological convergence across NLP subfields. These contributions lay foundational theoretical groundwork for NLG research and practice.

Technology Category

• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Natural language understanding for Web search
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

This article provides a brief overview of the field of Natural Language Generation. The term Natural Language Generation (NLG), in its broadest definition, refers to the study of systems that verbalize some form of information through natural language. That information could be stored in a large database or knowledge graph (in data-to-text applications), but NLG researchers may also study summarisation (text-to-text) or image captioning (image-to-text), for example. As a subfield of Natural Language Processing, NLG is closely related to other sub-disciplines such as Machine Translation (MT) and Dialog Systems. Some NLG researchers exclude MT from their definition of the field, since there is no content selection involved where the system has to determine what to say. Conversely, dialog systems do not typically fall under the header of Natural Language Generation since NLG is just one component of dialog systems (the others being Natural Language Understanding and Dialog Management). However, with the rise of Large Language Models (LLMs), different subfields of Natural Language Processing have converged on similar methodologies for the production of natural language and the evaluation of automatically generated text.

Problem

• Study systems verbalizing information via natural language
• Explore NLG applications like summarization and image captioning
• Examine NLG's relation to Machine Translation and Dialog Systems

Innovation

• Utilizes Large Language Models (LLMs)
• Integrates data-to-text generation
• Combines text and image captioning