Conversation

It astonishes me that people expected LLMs to be good at creating summaries. LLMs are good at transforms that have the same shape as ones that appear in their training data. They're fairly good, for example, at generating comments from code because code follows common structures and naming conventions that are mirrored in the comments (with totally different shapes of text).

In contrast, summarisation is tightly coupled to meaning. Summarisation is not just about making text shorter, it's about discarding things that don't contribute to the overall point and combining related things. This is a problem that requires understanding the material, because it's all about making value judgements.

So, it's totally unsurprising that the Australian study showed that it's useless. It's no surprise that both Microsoft and Apple's email summarisation tools discard the obvious phishing markers and summarise phishing scams as '{important thing happened}, click on this link' because they don't actually understand anything in the text that they're discarding, they just mark it as low entropy and discard it.