GPT, BART, TD-IDF ….

Photo by Aaron Burden on Unsplash

In my last article, I described how to use GPT-3 API to summarise articles from PDF.

Automated Summarisation of PDFs with GPT and Python

Although GPT (Generative Pre-trained Transformer) models are powerful and widely used for natural language processing tasks, they do have some limitations when it comes to summarisation:

• Length limitations: GPT models have a maximum output length, which can be a disadvantage when summarising long input texts, leading to a loss of accuracy.
• Repetition and redundancy: GPT models generates summaries word by word, without being able to “see” the entire summary as a whole. As a result, some phrases may be repeated or unnecessary information may be included.
• Difficulty with complex language: GPT models can struggle with understanding and summarising complex language, such as technical or scientific jargon.
• Dependence on training data: If the training data is not representative of the text to be summarised, or if the training data is biased in some way, the summaries produced by the model may be misleading.
• Data privacy concern: One potential…