Difference Between Large & Small Text Summarization
AI TO SUMMARIZE TEXT HOW TO
The growth in understanding of how to build and use chunking algorithms that keep the structure of contextual information and reduce data variance at runtime has been key as well. Past architectures such as LSTMs or RNNs were not as efficient nor as accurate as these transformer based models, which made long document summarization much harder. The key changes that have led to the new push in long text summarization are the introduction of transformer models such as BERT and GPT-3 that can handle much longer input sequences of text in a single run and a new understanding of chunking algorithms. The increased demand for the summarization of longer documents such as news articles and research papers has driven the growth in the space. The summarization space has grown rapidly with a new focus on handling super large text inputs to summarize down into a few lines. With the amount of time and resources required for manual summarization, it's no surprise that automatic summarization with NLP has grown across a number of different use cases for many different document lengths. Text summarization is an NLP process that focuses on reducing the amount of text from a given input while at the same time preserving key information and contextual meaning.
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