Automated text summarization is the process of using NLP tools to produce concise versions of text that preserve the key information present in the original content.
Good summaries can communicate the content with less text by retaining the key information while filtering out other information and noise (or useless text, if any). A shorter text may often take less time to read, and thus summarization facilitates more efficient use of time. The type of summarization that we are typically taught in school is abstractive summarization. One way to think of this is to consider abstractive summarization as a combination of understanding the meaning and expressing it in fewer sentences. It is usually considered as a supervised learning problem as the original text and the summary are both required.
Abstractive summarization is particularly hard for the machine. We have good extractive summarization models like pysummarization or othe TextRank based methods that summarize by identifying important sentences in a text and create summaries.
Models like Googls's T5 and Chat GPT-3 are abstractive model and represent state-of-the-art in summarization
Generating good quality Meta descriptions for large websites can be cumbersome, this can help with that. An additional use case is being able to summarize user feedback and complaints.
My next step is to do a similiar project in Chat GPT-3 and compare the quality of summarization betweeen it and Google T5. Google T5 biggest advantage right now is that its free, and thanks to HuggingFace easily accesible