Data-to-Text Generation: A Comprehensive Overview
Introduction:
Data-to-Text Generation is a challenging task in natural language understanding and generation that involves the conversion of structured data into fluently described text. In this form of NLG, the system takes input data, such as a table, and produces unambiguous and logically coherent text that adequately describes the data as output. Data-to-Text Generation is widely used in various fields, from assisting visually impaired people to generate descriptions of images to providing summarization of large datasets.
The Challenge:
The challenge of Data-to-Text Generation involves addressing at least two separate issues:
- What to say: The selection of an appropriate subset of the input data to discuss; and
- How to say it: The surface realization of generation.
Unlike machine translation, where the goal is to completely transduce the sentence, the Data-to-Text Generation concentrates on generating a text that accurately reflects the input data structure while adhering to the natural language syntax and semantics.
Approaches:
Several approaches have been proposed for Data-to-Text Generation, and most of them can be categorized into two major categories:
Template-based:
This approach involves defining pre-determined templates that can be used as a blueprint for generating text. The templates can range from simple sentence structures to more complex ones. The advantage of this approach is that it is typically easier to design, implement and maintain. However, the disadvantage of this approach is that it's inflexible, and it's challenging to create templates that cover all possible variations in input data. As a result, the generation can be unnatural, rigid and sometimes error-prone.
Machine Learning-based:
The machine learning approach involves training a neural network, typically a variant of the Recurrent Neural Network (RNN), to learn the mapping between input data and the generated text. Compared to the template-based method, Machine Learning-based methods can generate more diverse and fluent text. However, the challenge of this approach is that it requires large, paired input data and human-designed metrics to measure the quality of the generated text. Improperly chosen or biased metrics can lead to poor-quality outputs.
Advanced Approaches:
The state-of-the-art approaches involve utilizing both template-based and machine learning-based methods to generate high-quality, natural-sounding text. The two main forms of advanced approaches are:
Content selection-based:
This approach involves selecting the most relevant input data for generating text. Typically, the NLG system extracts and summarizes only the essential information required to make sense of the generated text. This helps avoid generating irrelevant or redundant information, leading to more concise and accessible text.
Planning-based:
This approach involves generating text in a more structured manner, where the NLG system plans the structure of the text before generating it. It takes into account factors like cohesion, discourse, rhetorical structure theory and attention-based encoding. This leads to more coherent, logical and well-structured text that can aid in better understanding of the input data.
Applications:
Data-to-Text Generation has numerous practical applications, including:
- Data journalism where an NLG system can produce news articles based on statistical reports and publicly available datasets.
- E-commerce where NLG can generate product descriptions automatically.
- Chatbots where the NLG system can provide information based on structured data input.
- Information systems that generate reports and summaries of databases.
- Automated medical reports based on electronic health records.
- Assistive technology for the visually impaired, where the NLG system can describe images, graphs and charts.
- Personalized recommendations and summaries on social media.
Conclusion:
Data-to-Text Generation is a crucial task in natural language understanding that involves converting structured data into meaningful and accurate text. It has numerous practical applications in industries like e-commerce, healthcare, journalism etc. Advances in NLG have led to the development of advanced approaches that incorporate content selection and planning techniques. These techniques result in more natural, well-structured and coherent text that can aid easier understanding of the input data.
