17  Paper Summaries

17.1 Summarise complex academic papers.

Simplifying academic literature into manageable summaries is a task that holds immense value, especially in a research or learning context. Yet, producing accurate, succinct, and accessible summaries of scholarly articles can be a daunting task. AI models, specifically those developed by OpenAI, are pushing the boundaries of what’s possible in this field, but understanding their capabilities and limitations is crucial.

Current summarisation tools tend to struggle with the extensive context window of research papers. Median length of a research paper hovers around 4,000 words, excluding the abstract and references, and 90% of papers range from 2,000 to 8,000 words. Most AI models have a context window limitation that can hinder their ability to process and summarise such lengthy texts.

For instance, the GPT-4 model, with a plus subscription, allows an 8k context window, which effectively processes approximately 6,000 words. However, as of 14 June 2023, OpenAI unveiled a new iteration of GPT-3.5, featuring a context window of 16k or about 13,500 words, comfortably within the range of even longer academic papers.

Unfortunately, the current user interface doesn’t allow direct PDF upload, so the text must be copied and pasted from the PDF or an HTML version of the paper. Moreover, the newly released “3.5-turbo-16k” model doesn’t work with the standard ChatGPT interface. For these features, you’ll need to access the OpenAI Playground, specifically loading the “3.5-turbo-16k” model. You can set the maximum output length based on your summary needs and paste your text. Requesting a summary is as simple as asking the model to do so.

Not only can you ask the model for a summary, but you can also probe for specifics about the methods, results, conclusions, or possible future directions. It’s important to note that this feature in the Playground isn’t free but is typically quite affordable.

What sets this summarisation technique apart is its ability to create a summary based on the actual text, rather than relying on embeddings or a “gist” of the information. Given the capacity for more extensive context understanding, this tool represents a significant advance in the realm of AI summarisation capabilities. As with any technological tool, it’s essential to use it wisely, ensure its output aligns with your understanding of the text, and always corroborate the findings with the original paper to maintain academic rigor.