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One of the most common NLP use cases is text similarity. Every domain comes with a variety of use cases. The most common uses of text similarity include finding related articles/news/genres, efficient use of search engines, classification of related issues on any topic, etc. It serves as a framework for many text analytics use cases. Methods to solve text similarity use cases have been around for a while, but the main drawbacks of the old methods are loss of dependency information, difficulty remembering long conversations, exploding gradient problems, etc. Recent advanced deep learning-based models pay attention to both contiguous and distant words, making their learning ability more rigorous. This white paper focuses on various text similarity techniques that can be used in everyday life to solve these use cases.

This paper demonstrates our work on the survey of pre-trained transformer models for text narration from tabular data. Understanding the meaning of data from tables or any other data source requires human effort and time to interpret the content. In this era of internet where data is exponentially growing and massive improvement in technology, we propose an NLP (Natural Language Processing) based approach where we can generate the meaningful text from the table without the human intervention. In this paper we propose transformer-based models with the goal to generate natural human interpretable language text generated from the input tables. We propose transformer based pre-trained model that is trained with structured and context rich tables and their respective summaries. We present comprehensive comparison between different transformer-based models and conclude with mentioning key points and future research roadmap.

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