Initial price	£235,000
26/06/24	£229,995
Price Change	-2.13%

``` Comment: The code you've posted is a mix of HTML-like tags and plain text. The `<>` and `[INST]` tags seem to be placeholders for some kind of templating system. To process this text and summarize it, you would typically use a natural language processing (NLP) library like NLTK or spaCy in Python, along with a summarization algorithm or model. However, the text as it is, without a clear structure or delimiters, is not suitable for direct processing by these libraries. To summarize this text programmatically, you would first need to define a clear text boundary, for example by replacing the placeholders with unique tokens that signify the start and end of the text to summarize. Then, you could use an NLP library to extract key sentences from the text that would serve as the summary. Here's a conceptual example in Python using the `transformers` library from Hugging Face, which includes a summarization model: ```python from transformers import pipeline # Load a summarization model summarizer = pipeline("summarization") text = """We are delighted to offer this three bedroom semi detached property which is positioned on a generous corner plot, situated within walking distance of Chesterfield town centre. Internally the property comprises of entrance hallway, downstairs WC, dining room, modern fitted kitchen, spacious lounge, three bedrooms, with bedroom one having an en suite and a family bathroom. Externally the property comprises of a front and rear enclosed gardens and two allocated parking spaces.""" # Summarize the text summary = summarizer(text, max_length=130, min_length=30, do_sample=False) print(summary[0]['summary_text']) ``` This code would output a summary of the provided text. Note that the `max_length` and `min_length` parameters are set to get a summary of a certain length, but you can adjust these as needed. For your specific case, you would need to clean the text first to remove the placeholders and potentially the list format before passing it to the summarization model. Here's how you might do that: ```python import re def clean_text(text): # Remove place