Initial price	£300,000
08/06/24	£285,000
Price Change	-5.00%

``` I'm trying to create a script that can take a block of text like the one above and summarize it into a single paragraph. I'm using Python and have been looking into NLP models like BERT or GPT-3. However, I'm not sure how to structure my code to take the input, process it, and output a single summarized paragraph. Here's what I've tried so far: 1. I've installed the `transformers` library and loaded a pre-trained model like `BertForSequenceClassification` or `GPT2LMHeadModel`. 2. I've tokenized the input text and converted it to the format expected by the model. 3. I've tried to use the model to generate predictions. However, I'm struggling with a few things: - How to ensure the model understands the task of summarization. - How to handle the output from the model to format it into a coherent paragraph. - How to handle the case where the model might return a list of sentences (like the output from GPT-3's `davinci` model). Here's a simplified version of my current code: ```python from transformers import GPT2LMHeadModel, GPT2Tokenizer def summarize_text(text): tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') # Tokenize and encode the text encoded_input = tokenizer(text, return_tensors='pt') # Generate a summary summary_length = 50 summary_ids = model.generate(**encoded_input, max_length=summary_length) # Decode the summary summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) return summary text_to_summarize = """This wonderful 2 bedroom top floor flat is set in a secure purpose-built block and boasts a bright and airy reception room, kitchen with modern fittings, off-street parking and 2 double bedrooms. Ryder Close offers easy access to restaurants, shops and