Prompt Title: pdf summary function

To automatically summarize a PDF paper using ChatGPT, you can follow these steps:

1. Convert the PDF paper into text format: You can use the PyPDF2 library in Python to extract the text from the PDF file. Here is an example code snippet:

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import PyPDF2

pdf_file = open('paper.pdf', 'rb')
pdf_reader = PyPDF2.PdfFileReader(pdf_file)

text = ''
for page_num in range(pdf_reader.getNumPages()):
    page = pdf_reader.getPage(page_num)
    text += page.extractText()

pdf_file.close()

2. Install the transformers library: Transformers is a powerful library for natural language processing and provides pre-trained models for text summarization. You can install it using pip:

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!pip install transformers

3. Load the pre-trained GPT-2 model for summarization: You can use the GPT-2 model for summarization by loading the t5-base model and fine-tuning it on a summarization dataset. Here is an example code snippet to load the model:

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from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained('t5-base')
model = T5ForConditionalGeneration.from_pretrained('t5-base')

4. Prepare the input for the model: The input to the model should be a string of text that needs to be summarized. You can use the encode_plus method of the tokenizer to prepare the input in the required format:

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inputs = tokenizer.encode_plus(text, max_length=1024, return_tensors='pt')

5. Generate the summary using the model: You can generate the summary by calling the generate method of the model and passing the prepared input as an argument:

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summary_ids = model.generate(inputs['input_ids'], num_beams=4, max_length=150, early_stopping=True)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)

Here, num_beams and max_length are the hyperparameters of the model that control the number of beams used in beam search and the maximum length of the generated summary, respectively.

6. Print the summary: You can print the generated summary using the following code:

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print(summary)

This should give you a summary of the PDF paper generated by the GPT-2 model. You can fine-tune the model on a summarization dataset to improve the quality of the generated summaries.